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To quickly achieve nondestructive detection of protein content in fresh milk, this study utilized a network analyzer and an open coaxial probe to analyze the dielectric spectra of milk samples at 100 frequency points within the 2–20 GHz range, focusing on the dielectric constant ε' and the dielectric loss factor ε''. Feature variables were extracted from the full dielectric spectra using the successive projections algorithm (SPA), uninformative variables elimination (UVE), and the combined UVE‐SPA method. These variables were then used to develop partial least squares regression (PLSR), support vector machine (SVM), decision tree (DT), random forest (RF), and least squares boosting (LSBOOST) models for predicting protein content. The results showed that ε' decreased monotonically with increasing frequency, while ε'' increased monotonically. The UVE‐SPA method for feature extraction demonstrated superior performance, with the UVE‐SPA‐PLSR model being the best for predicting milk protein content, achieving the highest RC2 = 0.998 and RP2 = 0.989 and the lowest RMSEC = 0.019% and RMSEP = 0.032%. This study provides a theoretical reference for evaluating milk quality and developing intelligent detection equipment for natural milk. [ABSTRACT FROM AUTHOR]

Early detection of plum bruising is important in the online postharvest quality sorting process. This paper explores the rapid detection of bruises in plum at five stages (1, 3, 6, 24, and 48 h after bruising) using hyperspectral imaging system. Spectral preprocessing was performed using five methods (unit vector normalization, multiplicative scattering correction, standard normal variate, detrending, and baseline). The support vector machine was established to discriminate the spectral samples of healthy and bruised plums at stage 1 on full wavelengths. The results indicated that the spectral data pretreated by multiple scattering correction yielded better results. The characteristic wavelengths of the spectra were selected by six algorithms (random frog, competitive adaptive reweighted sampling, variable combination population analysis, successive projection algorithm, uninformative variable elimination and bootstrapping soft shrinkage). Subsequently, the support vector machine was established at stage 1 based on these selected characteristic wavelengths. Comparing the results of models, the support vector machine based on the characteristic wavelengths selected by competitive adaptive reweighted sampling generated a satisfied effect with an accuracy of 95% for the calibration set and 98% for the prediction set. This model was selected for bruise detection in plums at the residual stage. The characteristic wavelength grayscale images selected based on the model were used to successfully visualize the plum bruise regions in five stages using minimum noise fraction transformation and the principal component analysis method. The results showed that the hyperspectral imaging technique combined with machine learning algorithm could be used to identify plum bruises at different stages. This study contributes to the development of an online detection system for bruises in plum. [ABSTRACT FROM AUTHOR]

Effectively detecting the quality of blueberries is crucial for ensuring that high-quality products are supplied to the fresh market. This study developed a nondestructive method for determining the soluble solids content (SSC) of blueberry fruit by using a near-infrared hyperspectral imaging technique. The reflection hyperspectral images in the 900–1700 nm waveband range were collected from 480 fresh blueberry samples. An image analysis pipeline was developed to extract the spectrums of blueberries from the hyperspectral images. A regression model for quantifying SSC values was successfully established based on the full range of wavebands, achieving the highest R P 2 of 0.8655 and the lowest RMSEP value of 0.4431 °Brix. Furthermore, three variable selection methods, namely the Successive Projections Algorithm (SPA), interval PLS (iPLS), and Genetic Algorithm (GA), were utilized to identify the feature wavebands for modeling. The models calibrated from feature wavebands generated an RMSEP of 0.4643 °Brix, 0.4791 °Brix, and 0.4764 °Brix, as well as the R P 2 of 0.8507, 0.8397, and 0.8420 for SPA, iPLS, and GA, respectively. Furthermore, a pseudo-color distribution diagram of the SSC values within blueberries was successfully generated based on established models. This study demonstrated a novel approach for blueberry quality detection and inspection by jointly using hyperspectral imaging and machine learning methodologies. It can serve as a valuable reference for the development of grading equipment systems and portable testing devices for fruit quality assurance. [ABSTRACT FROM AUTHOR]

Copyright of Modern Food Science & Technology is the property of Editorial Office of Modern Food Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to make up for the misjudgment of fish freshness by colorimetry due to the unclear color distinction, a naphthol-benzothiazole derivative (HBO) was synthesized by the condensation reaction of naphthol as a fluorophore with benzothiazole salt. HBO could recognize organic amines by colorimetric and fluorescent responses, and allow nondestructive detection of salmon freshness. HBO showed an obvious response for all 12 amines tested in an EtOH/H2O (1:9, V/V) system, had a low limit of detection (0.4 μmol/L for spermine), and could image for spermine in living cells. The sensing gel HBO agarose gel (HBOAL) was prepared by loading the probe onto agarose gels. When salmon flesh is stored with HBOAL, the fish can be judged to be spoiled if HBOAL exhibits a light red color in daylight and a yellow color in ultraviolet light. HBOAL distinguished clearly among the three grades of salmon fish freshness with accurate evaluation results, and therefore could be used to monitor the freshness of salmon fish in real time.

Abstract Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price. The traditional measuring method of lint percentage is labor-intensive and time-consuming; thus, an efficient and accurate measurement method is needed. In recent years, classification-based deep learning and computer vision have shown promise in solving various classification tasks. Results In this study, we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning. The model is deployed on a lint percentage detection instrument, which can rapidly and accurately determine the lint percentage of seed cotton. We evaluated the performance of the proposed approach using a dataset comprising 66 924 seed cotton images from different regions of China. The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%, with an average precision of 94.97%, an average recall of 95.26%, and an average F1-score of 95.20%. Furthermore, the proposed classification model achieved an average accuracy of 97.22% in calculating the lint percentage, showing no significant difference from the performance of experts (independent-sample t-test, t = 0.019, P = 0.860). Conclusion This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton. The proposed approach is a promising alternative to traditional methods, providing a rapid and accurate solution for the industry.

Based on the decorrelation calculation of diffusion ultrasound in time-frequency domain, this paper discusses the repeatability and potential significance of Disturbance Sensitive Zone (DSZ) in time-frequency domain. The experimental study of Barely Visible Impact Damage (BVID) on Carbon Fiber Reinforced Polymer (CFRP) is carried out. The decorrelation coefficients of time, frequency, and time-frequency domains and DSZ are calculated and compared. It has been observed that the sensitivity of the scattered wave disturbance caused by impact damage is non-uniformly distributed in both the time and frequency domains. This is evident from the non-uniform distribution of the decorrelation coefficient in time-domain and frequency-domain decorrelation calculations. Further, the decorrelation calculation in the time-frequency domain can show the distribution of the sensitivity of the scattered wave disturbance in the time domain and frequency domain. The decorrelation coefficients in time, frequency, and time-frequency domains increase monotonically with the number of impacts. In addition, in the time-frequency domain decorrelation calculation results, stable and repetitive DSZ are observed, which means that the specific frequency component of the scattered wave is extremely sensitive to the damage evolution of the impact region at a specific time. Finally, the DSZ obtained from the first 15 impacts is used to improve the decorrelation calculation in the 16-th to 20-th impact. The results show that the increment rate of the improved decorrelation coefficient is 10.22%. This study reveals that the diffusion ultrasonic decorrelation calculation improved by DSZ makes it feasible to evaluate early-stage damage caused by BVID. [ABSTRACT FROM AUTHOR]

Background: The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price. The traditional measuring method of lint percentage is labor-intensive and time-consuming; thus, an efficient and accurate measurement method is needed. In recent years, classification-based deep learning and computer vision have shown promise in solving various classification tasks. Results: In this study, we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning. The model is deployed on a lint percentage detection instrument, which can rapidly and accurately determine the lint percentage of seed cotton. We evaluated the performance of the proposed approach using a dataset comprising 66 924 seed cotton images from different regions of China. The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%, with an average precision of 94.97%, an average recall of 95.26%, and an average F1-score of 95.20%. Furthermore, the proposed classification model achieved an average accuracy of 97.22% in calculating the lint percentage, showing no significant difference from the performance of experts (independent-sample t-test, t = 0.019, P = 0.860). Conclusion: This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton. The proposed approach is a promising alternative to traditional methods, providing a rapid and accurate solution for the industry. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Chrysanthemum tea is popular because of its health benefits, although different varieties vary in effectiveness and role. The rapid identification of the variety is critical for consumers and the tea industry. This study employed near-infrared (NIR) spectroscopy in combination with feature extraction methods to characterize chrysanthemum tea varieties. To improve the recognition accuracy, a fuzzy improved pseudoinverse linear discriminant analysis (FIPLDA) algorithm was employed to extract discriminant information from the NIR spectra of chrysanthemum tea that were preprocessed using the Savitzky-Golay (SG) algorithm. Next, the dimensionality of the data was reduced by principal component analysis (PCA). The spectral features were extracted by pseudoinverse linear discriminant analysis (PLDA), improved pseudoinverse linear discriminant analysis (IPLDA), and FIPLDA. Five chrysanthemum tea samples were classified by k-nearest neighbor (KNN) and support vector machines (SVM), respectively. The results show that KNN performed better than SVM in classification, and the classification accuracy of FIPLDA-KNN reached 98.33%. Hence, the combination of NIR spectroscopy with FIPLDA suitable for distinguishing chrysanthemum tea. [ABSTRACT FROM AUTHOR]

Soybean polypeptide is a protein hydrolysate. It has multiple benefits, such as anticancer, antioxidant, etc. Addressing the limitations of traditional methods, such as time-consuming procedures and high costs, this study proposed a novel approach for the rapid, nondestructive detection of soybean polypeptide content. This approach utilizes the fusion of spectral and image information. First, seven preprocessing methods were applied to the spectral data. The results indicated that the model accuracy was highest after applying the standard normal variate preprocessing method. Second, a combined method, consisting of synergy interval partial least squares and iteratively retains informative variables, was proposed to extract characteristic variables. A two-layer Stacking framework ensemble learning model was constructed to compare the detection effects between single spectral input and fusion of spectral and image information input. The results showed that the fusion input model achieved the best performance. The coefficient of determination (R2) of prediction of the fusion input model reached 0.952, an improvement of 0.061 compared to single spectral input. And root mean square error of prediction (RMSEP) of the fusion input model reached 1.803 × 10−4, a reduction of 7.43 × 10−5 compared to single spectral input. These results indicate that the fusion of spectral and image information can effectively improve the model's accuracy, offering valuable technical support for soybean breeding and quality detection. [ABSTRACT FROM AUTHOR]

Berry fruits are highly nutritious and possess therapeutic properties, making them popular in various markets including fresh fruit, food, beauty, medical, and health. As people's quality of life continues to improve, the demand for berry fruits is increasing. As a result, farmers must prioritize the quality of berry fruits while also increasing production. In the realm of quality control, berry fruit detection holds great significance. However, traditional detection methods are plagued with major drawbacks such as destructiveness, high cost, and a long detection time. Fortunately, nondestructive testing technology has rapidly developed due to its nondamaging, efficient, and versatile advantages. This method can complete various detection projects and meet the diverse detection requirements of orchard supervision. This paper provides a review of the use of nondestructive testing technology in various types of berry fruits and highlights the progress made in optical nondestructive testing technology for identifying these fruits, as well as detecting their external and internal quality. This article summarizes and analyzes the challenges encountered by nondestructive testing in the same field of berry fruits and explores the potential development directions of nondestructive testing technology in the field. The findings of the study can offer valuable insights and reference for the intelligent management of berry orchards and the enhancement of the berry market system. [ABSTRACT FROM AUTHOR]

ObjectiveTraditional methods for detecting mold are time-consuming, labor-intensive, and vulnerable to environmental influences, highlighting the need for a swift, precise, and dependable detection approach. Researchers have utilized visible-near infrared (NIR) spectroscopy for the non-destructive, rapid assessment of wheat moisture content, crude protein content, concealed pests, starch content, dry matter, weight, hardness, origin, and other attributes. However, most of these studies rely on research-grade Visible-NIR spectrometers typically found in laboratories. While these spectrometers offer superior detection accuracy and stability, their bulky size, lack of portability, and high cost hinder their widespread use and adoption across various agricultural product distribution channels.MethodsA low-resolution Visible-NIR spectrometer (VNIAPD, with a resolution of 1.6 nm) was utilized to gather wheat data. The aim was to enhance the accuracy of moldy wheat detection by identifying suitable spectral data preprocessing methods using corresponding algorithms. A high-resolution Visible-NIR spectrometer (SINO2040, with a resolution of 0.19 nm) served as a control to validate the instrument and method's effectiveness. The Zhoumai (No. 22) wheat variety was adopted, with a total of 100 samples prepared. The spectra of fresh wheat were scanned and then placed in a constant temperature chamber at 35 °C to replicate the appropriate conditions for mold growth, thereby accelerating the reproduction of naturally occurring mold in the wheat. The degree of mold was categorized based on the cultivation time in the constant temperature chamber, with wheat classified as mildly, moderately, or severely moldy after 3, 6, and 9 days of cultivation, respectively. A total of 400 wheat spectral data points were collected, including 100 samples each of fresh wheat, wheat cultured for 3 days, wheat cultured for 6 days, and wheat cultured for 9 days. Preprocessing methods such as standard deviation normalization (SDN), standard normal variation (SNV), mean centrality (MC), first-order derivatives (1ST), Savitzky-Golay smoothing (SG), and multiple scattering correction (MSC) were applied to the spectral data. Outliers were identified and eliminated using the local outlier factor (LOF) method. Following this, the sequential projection algorithm (SPA) and Least absolute shrinkage and selection operator (LASSO) were used to extract characteristic wavelengths from the preprocessed spectra. Subsequently, six algorithms, including k-nearest neighbors (KNN), support vector machines (SVM), random forests (RF), Naïve-Bayes, back propagation neural networks (BPNN), and deep neural networks (DNN), were employed to model and analyze the feature wavelength spectra, differentiating moldy wheat and classifying the degree of mold. Evaluation criteria encompassed accuracy, modeling time, and model size to aid in selecting the most suitable model for specific application scenarios.Results and discussionsRegarding accuracy, even when utilizing the computationally slower and more memory-demanding neural network models BPNN and DNN, both the VNIAPD and SINO2040 achieved a perfect 100% accuracy in the binary classification task of distinguishing between fresh and moldy wheat. They also maintained a faultless 100% accuracy in the ternary classification task that differentiates three varying levels of mold growth. Adopting faster and more memory-efficient shallow models such as KNN, SVM, RF, and Naïve-Bayes, the VNIAPD yielded a top test set accuracy of 97.72% when combined with RF for binary classification. Conversely, SINO2040 achieved 100% accuracy using Naïve-Bayes. In the ternary classification scenario, the VNIAPD hit the mark at 100% accuracy with both KNN and RF, while SINO2040 demonstrated 97.72% accuracy with KNN and SVM. Regarding modeling speed, the shallow machine learning algorithms, including KNN, SVM, RF, and Naïve-Bayes, exhibited quicker training times, with Naïve-Bayes being the swiftest at just 3 ms. In contrast, the neural network algorithms BPNN and DNN required more time for training, taking 3 293 and 18 614 ms, respectively. Regarding memory footprint, BPNN had the largest model size, occupying 4 028 kb, whereas SVM was the most memory-efficient, with a size of only 4 kb. Overall, the VNIAPD matched the SINO2040 in detection accuracy despite having lower optical parameters: A slightly lesser optical resolution of 1.6 nm compared to the SINO2040's 0.19 nm—and a lower cost, highlighting its efficiency and cost-effectiveness in the given context.ConclusionsIn this study, by comparing different preprocessing methods for spectral data, the optimal data optimization choices for corresponding algorithms were identified. As a result, the low-resolution spectrometer VNIAPD was able to achieve performance on par with the high-resolution spectrometer SINO2040 in detecting moldy wheat, providing a new option for low-cost, non-destructive detection of wheat mold and the degree of moldiness based on Visible-NIR spectroscopy.

Objective: To propose a new solution to overcome the two challenges of data with spikes and small sample sizes in nectarine hyperspectral measurement. Methods: Based on hyperspectral imaging technology, image processing methods were used to identify the area of nectarines in the hyperspectral image, and the spectral reflectance data of the area was calculated to form a hyperspectral curve image. For hyperspectral image data with spikes and noise, compared the effects of several data preprocessing methods, including polynomial smoothing algorithm (SG), multivariate scatter correction algorithm (MSC), standard normal variate algorithm (SNV), first-order derivative operator (D1), and second-order derivative operator (D2) on model prediction accuracy. To address the high-dimensional and small sample size characteristics of the data, the principal component analysis algorithm (PCA) was used for dimensionality reduction, followed by outlier removal using the Mahalanobis distance measure method (MD). Finally, the Kennard-Stone algorithm (KS) was used to divide the data into training and testing sets, and the partial least squares regression (PLSR) model, which performed well in the small sample scenario, was selected for estimation and analysis of nectarine water content. Results: The SG-PCA-MD-KS-PLSR model performed best for estimating nectarine water content when there were spikes and noise in the hyperspectral curve. The coefficient of determination (R2) was 0.928, and the root mean square error (RMSE) was 0.008 4 on the training set. The R2 was 0.926, and the RMSE was 0.009 2 on the testing set. In further experiments grading nectarines based on their water content, the model's predictions showed good performance. The accuracy rate of grading was 0.956 for the training set and 0.923 for the testing set. Conclusion: By using hyperspectral imaging technology and establishing the SG-PCA-MD-KS-PLSR model, non-destructive estimation of nectarine water content and grading of nectarine water content can be achieved in scenarios with small hyperspectral sample sizes and noise.

Effectively detecting the quality of blueberries is crucial for ensuring that high-quality products are supplied to the fresh market. This study developed a nondestructive method for determining the soluble solids content (SSC) of blueberry fruit by using a near-infrared hyperspectral imaging technique. The reflection hyperspectral images in the 900–1700 nm waveband range were collected from 480 fresh blueberry samples. An image analysis pipeline was developed to extract the spectrums of blueberries from the hyperspectral images. A regression model for quantifying SSC values was successfully established based on the full range of wavebands, achieving the highest RP2 of 0.8655 and the lowest RMSEP value of 0.4431 °Brix. Furthermore, three variable selection methods, namely the Successive Projections Algorithm (SPA), interval PLS (iPLS), and Genetic Algorithm (GA), were utilized to identify the feature wavebands for modeling. The models calibrated from feature wavebands generated an RMSEP of 0.4643 °Brix, 0.4791 °Brix, and 0.4764 °Brix, as well as the RP2 of 0.8507, 0.8397, and 0.8420 for SPA, iPLS, and GA, respectively. Furthermore, a pseudo-color distribution diagram of the SSC values within blueberries was successfully generated based on established models. This study demonstrated a novel approach for blueberry quality detection and inspection by jointly using hyperspectral imaging and machine learning methodologies. It can serve as a valuable reference for the development of grading equipment systems and portable testing devices for fruit quality assurance.

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rice taste quality determines the satisfaction of consumers and whether it can be profitable for growers. The accurate and fast determination of the taste quality of rice helps in evaluating the commodity price of rice. In this study, partial least squares regression (PLSR) and near-infrared spectroscopy (NIR) were combined to predict changes in rice taste quality. The coefficient determination of cross-validation (R2 CV), the coefficient determination of prediction (R2 P), and the residual predictive deviation (RPD) of the PLSR model of the Savitzky-Golay first derivative spectrum were 0.84, 0.86, and 2.76, respectively. Three methods were applied to screen the valid wavelengths, including competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and random frog (RF). The RF algorithm screened 10 effective wavelengths and had the best prediction ability. After mining the effective wavelength by RF, the R2 CV , R2 P , and RPD were 0.89, 0.93, and 3.8, respectively. This study provided a rapid and accurate method for determining the taste quality of large-scale rice samples. [Display omitted] • First use of PLSR to mine and model the near-infrared spectrum of rice taste quality. • The RPD of the full wavelength Savitzky-Golay first derivation spectrum was 2.76 • Mining effective wavelengths by CARS, SPA, and RF algorithm. • The combined RF and PLSR models shown that R2 cv , R2 P , and RPD were 0.89, 0.93 and 3.8 • The combination of NIR and PLSR could quickly and accurately measure the taste quality. [ABSTRACT FROM AUTHOR]

[Objective] Traditional methods for detecting mold are time-consuming, labor-intensive, and vulnerable to environmental influences, highlighting the need for a swift, precise, and dependable detection approach. Researchers have utilized visible-near infrared (NIR) spectroscopy for the non-destructive, rapid assessment of wheat moisture content, crude protein content, concealed pests, starch content, dry matter, weight, hardness, origin, and other attributes. However, most of these studies rely on research-grade Visible-NIR spectrometers typically found in laboratories. While these spectrometers offer superior detection accuracy and stability, their bulky size, lack of portability, and high cost hinder their widespread use and adoption across various agricultural product distribution channels. [Methods] A low-resolution Visible-NIR spectrometer (VNIAPD, with a resolution of 1.6 nm) was utilized to gather wheat data. The aim was to enhance the accuracy of moldy wheat detection by identifying suitable spectral data preprocessing methods using corre‐ sponding algorithms. A high-resolution Visible-NIR spectrometer (SINO2040, with a resolution of 0.19 nm) served as a control to validate the instrument and method's effectiveness. The Zhoumai (No. 22) wheat variety was adopted, with a total of 100 samples pre‐ pared. The spectra of fresh wheat were scanned and then placed in a constant temperature chamber at 35°C to replicate the appropriate conditions for mold growth, thereby accelerating the reproduction of naturally occurring mold in the wheat. The degree of mold was categorized based on the cultivation time in the constant temperature chamber, with wheat classified as mildly, moderately, or se‐ verely moldy after 3, 6, and 9 days of cultivation, respectively. A total of 400 wheat spectral data points were collected, including 100 samples each of fresh wheat, wheat cultured for 3 days, wheat cultured for 6 days, and wheat cultured for 9 days. Preprocessing methods such as standard deviation normalization (SDN), standard normal variation (SNV), mean centrality (MC), first-order derivatives (1ST), Savitzky-Golay smoothing (SG), and multiple scattering correction (MSC) were applied to the spectral data. Outliers were iden‐ tified and eliminated using the local outlier factor (LOF) method. Following this, the sequential projection algorithm (SPA) and Least absolute shrinkage and selection operator (LASSO) were used to extract characteristic wavelengths from the preprocessed spectra. Subsequently, six algorithms, including k-nearest neighbors (KNN), support vector machines (SVM), random forests (RF), NaïveBayes, back propagation neural networks (BPNN), and deep neural networks (DNN), were employed to model and analyze the feature wavelength spectra, differentiating moldy wheat and classifying the degree of mold. Evaluation criteria encompassed accuracy, modeling time, and model size to aid in selecting the most suitable model for specific application scenarios. [Results and discussions] Regarding accuracy, even when utilizing the computationally slower and more memory-demanding neural net‐ work models BPNN and DNN, both the VNIAPD and SINO2040 achieved a perfect 100% accuracy in the binary classification task of distinguishing between fresh and moldy wheat. They also maintained a faultless 100% accuracy in the ternary classification task that differentiates three varying levels of mold growth. Adopting faster and more memory-efficient shallow models such as KNN, SVM, RF, and Naïve-Bayes, the VNIAPD yielded a top test set accuracy of 97.72% when combined with RF for binary classification. Conversely, SINO2040 achieved 100% accuracy using Naïve-Bayes. In the ternary classification scenario, the VNIAPD hit the mark at 100% accuracy with both KNN and RF, while SINO2040 demonstrated 97.72% accuracy with KNN and SVM. Regarding modeling speed, the shallow machine learning algorithms, including KNN, SVM, RF, and Naïve-Bayes, exhibited quicker training times, with Naïve-Bayes being the swiftest at just 3 ms. In contrast, the neural network algorithms BPNN and DNN required more time for train‐ ing, taking 3 293 and 18 614 ms, respectively. Regarding memory footprint, BPNN had the largest model size, occupying 4 028 kb, whereas SVM was the most memory-efficient, with a size of only 4 kb. Overall, the VNIAPD matched the SINO2040 in detection ac‐ curacy despite having lower optical parameters: A slightly lesser optical resolution of 1.6 nm compared to the SINO2040's 0.19 nm— and a lower cost, highlighting its efficiency and cost-effectiveness in the given context. [Conclusions] In this study, by comparing different preprocessing methods for spectral data, the optimal data optimization choices for corresponding algorithms were identified. As a result, the low-resolution spectrometer VNIAPD was able to achieve performance on par with the high-resolution spectrometer SINO2040 in detecting moldy wheat, providing a new option for low-cost, non-destructive detection of wheat mold and the degree of moldiness based on Visible-NIR spectroscopy. [ABSTRACT FROM AUTHOR]

In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low‐level, mid‐level, and high‐level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra‐to‐spectra, spectra‐to‐machine vision, spectra‐to‐electronic nose, and spectra‐to‐nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real‐world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone‐based multi‐sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis. [ABSTRACT FROM AUTHOR]

Soluble solid content (SSC), firmness, and color (L*, a*, and b*) are important physicochemical indices for assessing the quality and maturity of kiwifruits. Therefore, this research aimed to realize the nondestructive detection and visualization map for the physicochemical indices of kiwifruits at different maturity stages by hyperspectral imaging coupled with the chemometrics. To further improve the detection accuracy and working efficiency of the models, competitive adaptive reweighted sampling (CARS) and successive projection algorithm were employed to choose feature wavelengths for predicting the physicochemical indices of kiwifruits. Multiple linear regression (MLR) was designed to develop simplified detection models based on feature wavelengths for determining the physicochemical indices of kiwifruits. The results showed that 32, 18, 26, 29, and 32 feature wavelengths were extracted from 256 full wavelengths to predict the SSC, firmness, L*, a*, and b*, respectively, with the CARS algorithm. Not only was the working efficiency of the CARS-MLR model improved, but the prediction accuracy of the CARS-MLR model for determining the physicochemical indices was also at its relative best. The residual predictive deviations of the CARS-MLR model for determining the SSC, firmness, L*, a*, and b* were 3.09, 2.90, 2.32, 2.74, and 2.91, respectively, which were all above 2.3. Compared with the model based on the full spectra, the CARS-MLR model could be used to predict the physicochemical indices of kiwifruits. Finally, the visualization map for the physicochemical indices of kiwifruits at different maturity stages was generated by calculating the spectral response of each pixel on the kiwifruit samples with the CARS-MLR model. This made the detection for the physicochemical indices of kiwifruits more intuitive. This study demonstrates that hyperspectral imaging coupled with the chemometrics is promising for the nondestructive detection and visualization map for the physicochemical indices of kiwifruits, and also provides a novel theoretical basis for the nondestructive detection of kiwifruit quality.

In this study, a nondestructive vibro-acoustic setup was employed to acquire the vibro-acoustic signals of pear fruit. The signals were decomposed using the empirical mode decomposition (EMD). Different methods were used to suppress the end effect and mode mixing to achieve the optimal signal decomposition components. Then, the decomposition components of the vibro-acoustic signals were used as the input to construct a discriminant model based on convolution neural networks with spatial pyramid pool (CNN-SPP). The results showed that the improved slope-based method was better able to suppress the EMD end effect for the vibro-acoustic signals. The complementary complete ensemble empirical mode decomposition with adaptive noise (CCEEMDAN) method could exhibit better performance for suppressing mode mixing after end effect suppression. Thus, the obtained components were used as the input to construct a CCEEMDAN-CNN-SPP-based discriminant model. The overall classification accuracy of the model was 93.66% for pears with core browning, the discrimination accuracy was 94.44% for sub-healthy pears, and the misjudgment rate was 6.35% for diseased fruit. This improved the accuracy of vibro-acoustic identification of pears with early-stage mild disease. This study lays a foundation for the development of an online detection system for sub-healthy fruits in the future.

Models for maturity discrimination and polyphenol content determination of Aronia melanocarpa (AM) were established to nondestructively determinethe maturityand polyphenol content of AM based on the hyperspectral imaging (HSI) technology. The HSI technology was adopted to collect the image information of Fukangyuan No. 1 AM in different maturity levels, and the Folin-Ciocalteu colorimetric method was employed to determine the polyphenol content. The Monte Carlo method was used to eliminate the outliers. The original image information was preprocessed with the following procedures: Moving average, median filter, normalize, baseline calibration, multiple scattering correction, detrending, and standard normal variate. Meanwhile, the sample set partitioning based on joint x-y distance method was applied to divide the samples. The competitive adaptive reweighting sampling and uninformative variable elimination method were selected to extract the feature wavelengths, based on which the partial least squares model (PLS) and support vector machine (SVM) model were established and compared. The results showed that the UVE-SVM model after multiple scattering correction showed the best performance among all established discriminant models, with the comprehensive recognition rate of 94.62%, Rc2 of 0.9712, and accuracy of 100%. The CARS-SVM model after median filter exhibited the best efficiency in detection of polyphenol content, with the Rc2 of 0.8331. In addition, this work proved that the polyphenol content in AM was visual. This work provided a theoretical basis for the application of HSI technology in berry industry.

Obtaining detailed information about the design and construction of ancient stone pagodas is challenging due to their significant age. Due to this, the internal structure is still unknown, and the analysis of their structural safety lacks a solid foundation. The purpose of this paper is to research ancient stone pagodas and propose adequate methods for identifying their internal structure and assessing their damage status using ground-penetrating radar nondestructive detection technology. The internal structure of the pagoda is inferred through a comprehensive process of layer-by-layer scanning using shielded antennas of varying frequencies, incorporating distance correction. The results demonstrate that by selecting suitable radar antenna frequencies, both test profiles become visible, and the position and characteristics of the internal substantial reflection area exhibit a high degree of consistency, mutually reinforcing each other. The White Pagoda in Hangzhou is believed to have both a mortise and tenon joint structure and a column structure. The ground penetrating radar detection method proves to be effective and has valuable applications.

Potassium is one of the indispensable nutrient elements for plant growth, fruit development, and yield. The research and application of potassium nutrition diagnosis technology is the premise of scientific potassium management. However, potassium deficiency in tomato leaves, from vegetative to reproductive growth, is not easy to diagnose. To alleviate this problem, this paper proposes a suitable method of supplying potassium to tomatoes via a nutrient solution and diagnosing potassium abundance and deficiency through diagnosis methods based on ecological morphology, biological accumulation, and the photosynthetic characteristics of tomato plants. The relationship between the ecological morphology and biomass accumulation of tomatoes cultivated in the nutrient solution with potassium supply levels of 1, 4, 8, and 16 mmol/L is also discussed, and the potassium supply in the nutrient solution was studied 21 days after transplanting. The results showed that there was a significant quadratic correlation between the potassium supply in the nutrient solution and plant height and biomass accumulation, respectively. The most suitable level of potassium supply via the nutrient solution was deemed to be 10~13 mmol/L. However, if irreversible damage or severe stress to tomato plants has occurred because of potassium deficiency, there will be serious differences in the growth status of plants, and the diagnosis results will deviate greatly. In addition, the photosynthetic induction characteristics responding to the dark–light conversion of tomato leaves with potassium contents of 0.9%, 2.1%, 3.1%, and 3.3% cultivated with potassium supply amounts of 1, 4, 8, and 16 mmol/L in the nutrient solution were investigated. The results showed that tomato leaves with potassium contents of 3.1% and 3.3% had a more rapid response to dark–light conversion and higher first-order derivatives of net photosynthetic rate compared to those with potassium contents of 0.9% and 2.1%, but the first-order derivative of intercellular CO2 concentration showed an opposite trend. Additionally, a quadratic correlation between leaf potassium content and CO2 assimilation during 5 min of photosynthetic induction was established (R2 > 0.99). According to this correlation, the suitable leaf potassium content was estimated to be 2.3~2.7%, similar to that of tomatoes cultured in the nutrient solution with a 4~8 mmol/L potassium supply. Therefore, this method can realize the rapid, non-destructive, and real-time detection of potassium content in tomato leaves based on a portable photosynthetic measurement system by establishing the relationship between leaf potassium content and net CO2 assimilation during the photosynthetic induction period, therefore helping to avoid the irreversible damage caused by potassium deficiency at the later stages of plant cultivation and providing technical support for the precise fertilization of potassium in actual cultivation. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The work aimed to improve the prediction efficiency in rapid determination of the firmness of kiwifruit. A Vis/NIR (390~1030 nm) hyperspectral imaging system was applied to obtain the images of Guichang kiwifruit, and the reflective spectrum in the regions of interest on each sample was acquired. Noise from original reflective spectrum was reduced by the standard normal variation method. The competitive adaptive reweighted sampling (CARS) and the successive projection algorithm were applied to select feature variables. Finally an error back propagation neural network and a multi linear regression (MLR) model were constructed to predict the firmness of kiwifruit. A total of 35 feature variables were selected by CARS from 256 variables. The working efficiency of the final prediction model was improved by 11-fold, with the runtime dropped from 5.84 s to 0.54 s. Overall, the CARS-MLR model showed a relatively good detection capability (rc=0.95, rp=0.92, RMSEC=1.65 kg/cm2, RMSEP=1.99 kg/cm2, RPD above 2). This study demonstrated the application potential of the nondestructive hyperspectral imaging technology for fast determination of the firmness of kiwifruit.

Based on the decorrelation calculation of diffusion ultrasound in time-frequency domain, this paper discusses the repeatability and potential significance of Disturbance Sensitive Zone (DSZ) in time-frequency domain. The experimental study of Barely Visible Impact Damage (BVID) on Carbon Fiber Reinforced Polymer (CFRP) is carried out. The decorrelation coefficients of time, frequency, and time-frequency domains and DSZ are calculated and compared. It has been observed that the sensitivity of the scattered wave disturbance caused by impact damage is non-uniformly distributed in both the time and frequency domains. This is evident from the non-uniform distribution of the decorrelation coefficient in time-domain and frequency-domain decorrelation calculations. Further, the decorrelation calculation in the time-frequency domain can show the distribution of the sensitivity of the scattered wave disturbance in the time domain and frequency domain. The decorrelation coefficients in time, frequency, and time-frequency domains increase monotonically with the number of impacts. In addition, in the time-frequency domain decorrelation calculation results, stable and repetitive DSZ are observed, which means that the specific frequency component of the scattered wave is extremely sensitive to the damage evolution of the impact region at a specific time. Finally, the DSZ obtained from the first 15 impacts is used to improve the decorrelation calculation in the 16-th to 20-th impact. The results show that the increment rate of the improved decorrelation coefficient is 10.22%. This study reveals that the diffusion ultrasonic decorrelation calculation improved by DSZ makes it feasible to evaluate early-stage damage caused by BVID.

An analytical integration Legendre polynomial series approach (AILPSA) is proposed to study guided waves in a resin bolt, which is a typical layered cylindrical structure. Compared with the conventional Legendre polynomial series approach, the AILPSA can save the abundant numerical integration calculation time and dramatically improve the computational efficiency. The attenuation and group velocity curves of the resin bolts are calculated. The errors of group velocity between the theoretical and the experimental are analyzed, and the contrast diagrams between amplitude curves of end reflection and attenuation curves are given to illustrate the law between these curves. [ABSTRACT FROM AUTHOR]

The flavor of Pomelo is highly variable and difficult to determine without peeling the fruit. The quality of pomelo flavor is due largely to the total soluble solid content (TSSC) in the fruit and there is a commercial need for a quick but nondestructive TSSC detection method for the industrial grading of pomelo. Due to the large size and thick mesocarp of pomelo, determining the internal quality of a pomelo fruit in a nondestructive manner is difficult, and the detection accuracy is further complicated by the noise typically generated by the common methods for the internal quality detection of other fruits. Thus, the aim of this study was to determine the optimal method to accurately detect pomelo TSSC and find a de-noising model which reduces the influence of noise on the optimal method's results. After developing a full-transmission visible/near infrared (VIS/NIR) spectroscopy sampling method, the confirming experimental results showed that the optimal pomelo TSSC detection model was Savitzky Golay + standard normal variate + competitive adaptive reweighted sampling + partial least squares regression. The R2 and RMSE of the calibration set for pomelo TSSC detection were 0.8097 and 0.8508, respectively, and the R2 and RMSE of the validation set for pomelo TSSC detection were 0.8053 and 0.8888, respectively. Both reference and dark de-noising are important for pomelo internal quality detection and should be calibrated frequently to compensate for time drift. This study found that large sensor response translation noise can be reduced with an artificial horizontal shift. Data supplementation is efficient for improving the adaption of the detection model for batch differences in pomelo samples. Using this optimized de-noising model to compensate for time drift, sensor response translation, and batch differences, the developed detection method is capable of satisfying the requirements of the industry (TSSC detection R2 was equal or larger than 0.9, RMSE was less than 1). These results indicate that full-transmission VIS/NIR spectroscopy can be exploited to realize the nondestructive detection of pomelo TSSC on an industrial scale, and that the methodologies used in this study can be immediately implemented in real-world production. [ABSTRACT FROM AUTHOR]

Water plays a very important role in the growth of tomato (Solanum lycopersicum L.), and how to detect the water status of tomato is the key to precise irrigation. The objective of this study is to detect the water status of tomato by fusing RGB, NIR and depth image information through deep learning. Five irrigation levels were set to cultivate tomatoes in different water states, with irrigation amounts of 150%, 125%, 100%, 75%, and 50% of reference evapotranspiration calculated by a modified Penman-Monteith equation, respectively. The water status of tomatoes was divided into five categories: severely irrigated deficit, slightly irrigated deficit, moderately irrigated, slightly over-irrigated, and severely over-irrigated. RGB images, depth images and NIR images of the upper part of the tomato plant were taken as data sets. The data sets were used to train and test the tomato water status detection models built with single-mode and multimodal deep learning networks, respectively. In the single-mode deep learning network, two CNNs, VGG-16 and Resnet-50, were trained on a single RGB image, a depth image, or a NIR image for a total of six cases. In the multimodal deep learning network, two or more of the RGB images, depth images and NIR images were trained with VGG-16 or Resnet-50, respectively, for a total of 20 combinations. Results showed that the accuracy of tomato water status detection based on single-mode deep learning ranged from 88.97% to 93.09%, while the accuracy of tomato water status detection based on multimodal deep learning ranged from 93.09% to 99.18%. The multimodal deep learning significantly outperformed the single-modal deep learning. The tomato water status detection model built using a multimodal deep learning network with ResNet-50 for RGB images and VGG-16 for depth and NIR images was optimal. This study provides a novel method for non-destructive detection of water status of tomato and gives a reference for precise irrigation management. [ABSTRACT FROM AUTHOR]

In this paper, hyperspectral imaging technology was used for nondestructive detection and distribution visualization of total acidity and firmness of red globe grapes. The hyperspectral information of 360 samples of growing red globe grapes in the wavelength range from 450 to 1 000 nm was collected using a hyperspectral instrument, and the total acidity and firmness of these samples were determined by titration and a texture analyzer, respectively. The Kennard-Stone (KS) algorithm was used to divide the total samples into a training set (270 samples) and a test set (90 samples) in a 3:1 ratio. The collected raw spectral data were preprocessed using various methods such as standard normal variate (SNV), Savitzky-Golay (SG), multivariate scatter correction (MSC), and normalization to determine the best spectral preprocessing method. Then, the feature variables were extracted from the spectral information using six dimensionality reduction algorithms: competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), genetic algorithm (GA), uninformative variable elimination (UVE), CARS-SPA, and UVE-SPA. Using partial least squares regression (PLSR), optimal prediction models for total acidity and firmness were developed separately. Finally, the total acidity and hardness for each pixel of the hyperspectral image were calculated according to the proposed optimal prediction models, and a gray scale image was obtained and pseudo-color transformed to visualize the distribution of total acidity and firmness of red globe grapes. The results showed that the optimal prediction model for total acidity was MSC-CARS-SPA-PLSR, with correlation coefficient for the prediction set (Rp), root mean square errors of prediction (RMSEP) and residual predictive deviation (RPD) of 0.985 1, 1.348 2 and 5.664 3, respectively. The optimal prediction model for firmness was SG-CARS-PLSR, with Rp, RMSEP and RPD of 0.929 1, 7.935 4 and 2.510 8, respectively. In summary, hyperspectral imaging provides a new method for the detection and visualization of total acidity and firmness of growing red globe grapes.

Terahertz time‐domain spectroscopy (THz‐TDS) has become an emerging nondestructive detection technique due to the unique properties of THz waves; it offers safety, penetrability, and fingerprint identification, especially for commonly used non‐polar materials. Here, we measure the differences in THz spectra of four wood blocks (manglietia, amur linden, black walnut, and ebony) in the frequency range of 0.1–0.9 THz. In addition, we demonstrate the principal components analysis (PCA) applied in THz absorption spectra; the principal components extracted from original data by PCA can replace the original absorption coefficient data and can clearly distinguish the wood types. Results show that THz‐TDS combined with PCA can be used as an effective nondestructive identification method in the field of public security, food detection, and customs anti‐smuggling initiatives. [ABSTRACT FROM AUTHOR]

BACKGROUND: The characteristics of corn kernels are strongly connected with the content of three statuses of water: bound water, immobilized water, and free water. Monitoring different water contents is very important to optimize the drying process, improve corn quality, and reduce energy consumption. The feasibility of nondestructive detection of water status and its distribution in corn kernels during the hot‐air drying process using multispectral imaging was investigated. RESULTS: The chemometric methods used to develop prediction models were back propagation neural network, least‐squares support vector machine, and partial least squares. The back propagation neural network achieved the best prediction performance for total and free water contents, with correlation coefficient of prediction (Rp) of 0.9717 and 0.9782 respectively, root‐mean‐square error of prediction (RMSEP) of 4.48% and 2.54% respectively, and ratio of prediction to deviation (RPD) of 4.87 and 4.29 respectively. And partial least squares was better for the prediction of immobilized and bound water contents, with Rp of 0.9612 and 0.9798 respectively, RMSEP of 0.57% and 0.06% respectively, and RPD of 4.78 and 4.42 respectively. CONCLUSION: It could be concluded that multispectral imaging combined with chemometric methods would be a promising technique for rapid and nondestructive detection of water status and its distribution in corn kernels. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

In order to the quickly and nondestructively detect whether starch is adulterated in minced chicken meat, a novel method combining hyperspectral imaging (HSI) technique with transfer learning was proposed in this study. First of all, hyperspectral images of minced chicken meat with different mass fractions of starch were collected and spectral information of the samples in the range of 400.89–1000.19 nm was extracted. Then, the hyperspectral data was preprocessed via continuous wavelet transform (CWT), which transformed the pixel‐level hyperspectral data into two‐dimensional spectrograms. Furthermore, classification model for identifying starch in minced chicken meat was constructed based on the GoogLeNet network pretrained on ImageNet data set. Finally, the support vector machine (SVM) model and the convolutional neural network (CNN) model without transfer learning were established for comparison. The results indicated that the model based on GoogLeNet network had a higher classification accuracy, up to 98.6%. Therefore, this study demonstrates the feasibility of the detection of starch in minced chicken meat based on HSI technique and transfer learning. Practical applications: The identification of starch in the minced chicken meat is of particular significance for maintaining the market order and safeguarding the human health. An innovative method based on hyperspectral imaging technique and transfer learning to identify whether the minced chicken meat mixed with starch was proposed in this study. The method achieved quickly, nondestructively and accurately detection of starch in minced chicken meat. [ABSTRACT FROM AUTHOR]

Fish are prone to spoilage and deterioration during processing, storage, or transportation. Therefore, there is a need for rapid and efficient techniques to detect and evaluate fish freshness during different periods or conditions. Gas sensors are increasingly important in the qualitative and quantitative evaluation of high‐protein foods, including fish. Among them, metal–oxide–semiconductor resistive (MOSR) sensors with advantages such as low cost, small size, easy integration, and high sensitivity have been extensively studied in the past few years, which gradually show promising practical application prospects. Herein, we take the detection, classification, and assessment of fish freshness as the actual demand, and summarize the physical and chemical changes of fish during the spoilage process, the volatile marker gases released, and their production mechanisms. Then, we introduce the advantages, performance parameters, and working principles of gas sensors, and summarize the MOSR gas sensors aimed at detecting different kinds of volatile marker gases of fish spoiling in the last 5 years. After that, this paper reviews the research and application progress of MOSR gas sensor arrays and electronic nose technology for various odor indicators and fish freshness detection. Finally, this review points out the multifaceted challenges (sampling system, sensing module, and pattern recognition technology) faced by the rapid detection technology of fish freshness based on metal oxide gas sensors, and the potential solutions and development directions are proposed from the view of multidisciplinary intersection. [ABSTRACT FROM AUTHOR]

The detection of poultry egg microcracks based on electrical characteristic models is a new and effective method. However, due to the disorder, mutation, nonlinear, time discontinuity, and other factors of the current data, detection algorithms such as support-vector machines (SVM) and random forest (RF) under traditional statistical characteristics cannot identify subtle defects. The detection system voltage is set to 1500 V in the existing method, and higher voltages may cause damage to the hatched eggs; therefore, how to reduce the voltage is also a focus of research. In this paper, to address the problem of the low signal-to-noise ratio of microcracks in current signals, a wavelet scattering transform capable of extracting translation-invariant and small deformation-stable features is proposed to extract multi-scale high-frequency feature vectors. In view of the time series and low feature scale of current signals, various convolutional networks, such as a one-dimensional convolutional neural network (1DCNN), long short-term memory (LSTM), bi-directional long short-term memory (Bi-LSTM), and gated recurrent unit (GRU) are adopted. The detection algorithm of the wavelet scattering convolutional network is implemented for electrical sensing signals. The experimental results show that compared with previous works, the accuracy, precision, recall, F1-score, and Matthews correlation coefficient of the proposed wavelet scattering convolutional network on microcrack datasets smaller than 3 μ m at a voltage of 1000 V are 99.4393%, 99.2523%, 99.6226%, 99.4357%, and 98.8819%, respectively, with an average increase of 2.0561%. In addition, the promotability and validity of the proposed detection algorithm were verified on a class-imbalanced dataset and a duck egg dataset. Based on the good results of the above experiments, further experiments were conducted with different voltages. The new feature extraction and detection method reduces the sensing voltage from 1500 V to 500 V, which allows for achieving higher detection accuracy with a lower signal-to-noise ratio, significantly reducing the risk of high voltage damage to hatching eggs and meeting the requirements for crack detection. [ABSTRACT FROM AUTHOR]

The nitrogen content is an important indicator affecting corn plants' growth status. Most of the standard hyperspectral imaging-based techniques for nondestructive detection of crop nitrogen content use a single feature as the input variable of the model, which reduces the generalization ability of the prediction model. To this end, a prediction model for the nitrogen content of corn leaves based on the fusion of image and spectral features is proposed. In this study, corn leaves at the modulation stage were studied, samples with different nitrogen levels were numbered, and their hyperspectral data in the wavelength range of 400~1100 nm were collected. The average spectrum of the models was used as valid spectral information. First-order derivatives, standard normal variables transformation (SNV), Savitzky-Golay (S-G) smoothing, and normalization were selected to preprocess the spectral features. The CARS-SPA algorithm was used to screen sensitive spectral variables. The gray level co-currency matrix (GLCM) was chosen to extract the texture image features of the test samples. Corn leaf spectral and texture image features were fused and modeled as target features. Partial least squares regression (PLSR) and support vector machine regression (SVR) were used to predict corn leaves' nitrogen content. The results showed that the image and spectral-based fusion models improved the prediction performance to some extent compared to the univariate models. The PLSR model based on feature fusion predicted the best results, in which the RP2 and RMSEP were 0.987 and 0.047. This method provides a reliable theoretical basis and technical support for developing nondestructive and accurate detection of nitrogen content in corn leaves. [ABSTRACT FROM AUTHOR]

Tea polyphenols, amino acids, soluble sugars, and other ingredients in fresh tea leaves are the key parameters of tea quality. In this research, a tea leaf ingredient estimation sensor was developed based on a multi-channel spectral sensor. The experiment showed that the device could effectively acquire 700–1000 nm spectral data of tea tree leaves and could display the ingredients of leaf samples in real time through the visual interactive interface. The spectral data of Fuding white tea tree leaves acquired by the detection device were used to build an ingredient content prediction model based on the ridge regression model and random forest algorithm. As a result, the prediction model based on the random forest algorithm with better prediction performance was loaded into the ingredient detection device. Verification experiment showed that the root mean square error (RMSE) and determination coefficient (R2) in the prediction were, respectively, as follows: moisture content (1.61 and 0.35), free amino acid content (0.16 and 0.79), tea polyphenol content (1.35 and 0.28), sugar content (0.14 and 0.33), nitrogen content (1.15 and 0.91), and chlorophyll content (0.02 and 0.97). As a result, the device can predict some parameters with high accuracy (nitrogen, chlorophyll, free amino acid) but some of them with lower accuracy (moisture, polyphenol, sugar) based on the R2 values. The tea leaf ingredient estimation sensor could realize rapid non-destructive detection of key ingredients affecting tea quality, which is conducive to real-time monitoring of the current quality of tea leaves, evaluating the status during tea tree growth, and improving the quality of tea production. The application of this research will be helpful for the automatic management of tea plantations. [ABSTRACT FROM AUTHOR]

Anthocyanin is one of the most landmark components in Lonicera japonica Thunb. The content of anthocyanin can be utilized to determine the timely harvest time and the quality of fresh fruit. The chemical methods are commonly used to detect the anthocyanin content in berries under laboratory conditions, including spectrophotometry, pH difference, and chromatography. The standard determination of anthocyanins in fruits and vegetables can present high accuracy and wide application. But, online and real-time detection is limited to the long time consuming and strong specialization. It is feasible to predict and evaluate the internal quality of agricultural products, according to their appearance and color characteristics. Taking the Lonicera edulis fruit as the research object, this study aims to determine the relationship between the appearance color indexes and the anthocyanins content under different growth periods. Gaussian processes regression (GPR) model was established to characterize the anthocyanin content as the function of characteristic color indexes of Lonicera edulis fruits. The nondestructive detection of anthocyanin content was also realized in Lonicera edulis fruit. The results indicate that the anthocyanin content of fruit increased rapidly from the fourth stage of Lonicera edulis growth, and the second stage of half color transition. The highest level was achieved at the seventh stage of maturity, during which the fruit color changed from the red to the purple black. During the growth and ripening of Lonicera edulis fruit, 21 color characteristic parameters were formed by the single component of RGB tri-color and HSV color saturation of the fruit and the combined component obtained by arithmetic operation. These parameters were significantly correlated with anthocyanin content (P<0.01); Based on the normalized difference of fruit green and blue primary colors, the optimal Gaussian model of anthocyanin content was established with high accuracy (R² c=0.993) and low error (CRMSE=4.752 mg/100g). The typical prediction models of anthocyanin content in Lonicera edulis included multiple linear regression, neural network, Gaussian function, and support vector machine. A comparison was made for the main evaluation indicators of the typical modeling, from the aspects of the fitting degree, mean square error, the number of required factors, and the complexity of the model. The high prediction accuracy, low deviation, and simple structure were achieved in the Gaussian function model with the characteristic color value of Lonicera edulis to predict the anthocyanin content, indicating superior to the other three prediction models. In particular, the prediction of anthocyanin content with one characteristic parameter g-b (X21) reduced the illumination interference of environmental factors, such as reflection, indicating the practical significance. Multivariate linear regression model and support vector machine model presented a lower fitting degree, more required factors, and more complicated color information processing, but these two models also had higher practicability. In the artificial neural network (ANN) model, all 21 characteristic parameters were required to participate with the low fitting degree, indicating the relatively low applicability at present. The optimal Gaussian prediction model performed fast with less amount of data, suitable for the prediction of the anthocyanin content of Lonicera edulis fruit. As such, the real-time evaluation of interesting quality indicators can be realized after the non-destructive determination of anthocyanin content during the growth period of Lonicera edulis. The finding can also provide guidance for the determination of rapid, non-destructive, and high-accuracy detection and equipment design of anthocyanin content in Lonicera edulis during growth and harvest. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The timely detection of information on crop nutrition is of great significance for improving the production efficiency of facility crops. In this study, the terahertz (THz) spectral information of tomato plant leaves with different nitrogen levels was obtained. The noise reduction of the THz spectral data was then carried out by using the Savitzky-Golay (S-G) smoothing algorithm. The sample sets were then analyzed by using Kennard-Stone (KS) and random sampling (RS) methods, respectively. The KS algorithm was optimized to divide the sample sets. The stability competitive adaptive reweighted sampling (SCARS), uninformative variable elimination (UVE), and interval partial least-squares (iPLS) algorithms were then used to screen the pre-processed THz spectral data. Based on the selected characteristic frequency bands, a model for the detection of the nitrogen content of tomato based on the THz spectrum was established by the radial basis function neural network (RBFNN) and backpropagation neural network (BPNN) algorithms, respectively. The results show that the root-mean-square error of correction (RMSEC) and root-meansquare error of prediction (RMSEP) of the BPNN model were respectively 0.1722% and 0.1843%, and the determination coefficients of the correction set (Rc²) and prediction set (Rp²) were respectively 0.8447 and 0.8375. The RMSEC and RMSEP values of the RBFNN model were respectively 0.1322% and 0.1855%, and the Rc² and Rp² values were respectively 0.8714 and 0.8463. Thus, the accuracy of the model established by the RBFNN algorithm was slightly higher. Therefore, the nitrogen content of tomato leaves can be detected by THz spectroscopy. The results of this study can provide a theoretical basis for the research and development of equipment for the detection of the nitrogen content of tomato leaves. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Modern Food Science & Technology is the property of Editorial Office of Modern Food Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Display omitted] • Accurate recognition for different degree of fumigated grains was realized by HSI. • A new method for chemical constituents contents in fumigation grains were developed. • Prediction models of MC,TA and AAN contents were constructed and compared. • HSI and chemometrics were used to estimate quality of fumigated grains of SAV. • SNV-CARS-PLS models showed the best results in modeling. The quality of the grains during the fumigation process can significantly affect the flavour and nutritional value of Shanxi aged vinegar (SAV). Hyperspectral imaging (HSI) was used to monitor the extent of fumigated grains, and it was combined with chemometrics to quantitatively predict three key physicochemical constituents: moisture content (MC), total acid (TA) and amino acid nitrogen (AAN). The noise reduction effects of five spectral preprocessing methods were compared, followed by the screening of optimal wavelengths using competitive adaptive reweighted sampling. Support vector machine classification was employed to establish a model for discriminating fumigated grains, and the best recognition accuracy reached 100%. Furthermore, the results of partial least squares regression slightly outperformed support vector machine regression, with correlation coefficient for prediction (Rp) of 0.9697, 0.9716, and 0.9098 for MC, TA, and AAN, respectively. The study demonstrates that HSI can be employed for rapid non-destructive monitoring and quality assessment of the fumigation process in SAV. [ABSTRACT FROM AUTHOR]

Cracking is a widespread phenomenon throughout the service of concrete structures for marine and offshore engineering. In this paper, the effect of sulfate ions and related cations on the crack closure behavior of cracked mortar in chloride-containing environments was studied. To this end, the mortars are cracked before transferring to the pure chloride solution as well as composite chloride and sulfate solutions (CNS and CMS series). The self-healing behavior is assessed by the evolution of crack width, water absorption, and nondestructive ultrasonic detection. The Vickers hardness test is applied to evaluate the micro-hardness of self-healing products generated in the crack gaps. Thermodynamic modeling of the phase volume expansion from the mortar matrix to the crack surface is conducted using Gibb's free energy theory to explore the self-healing mechanisms. Experiment results suggest that the existence of sulfate ions in chloride-containing solutions is beneficial for crack closure, and self-healing could be more obvious if the cations that bind to sulfate are magnesium ions. The distributions of Vickers hardness on the mortar surface across the crack reveal that the brucite layer diminishes the overall Vickers hardness of the mortar matrix, whereas it is favorable for the micro-hardness enhancement of self-healing products in the CMS series. Thermodynamic modeling interprets the expansion rates relative to the original unhydrated cement under the action of different soaking solutions. • Double or triple layer precipitates were found in CMS series. • Healing efficiency ordered as CMS-2 > CMS-1 > CNS-2 > CNS-1 > pure chloride. • Waveform evolution of ultrasonic pulse signals improved in the self-healing process. • Brucite layer was favorable for the micro-hardness enhancement of healing products. • Thermodynamic modeling confirmed the volume expansion in CNS and CMS series. [ABSTRACT FROM AUTHOR]

Potassium is one of the indispensable nutrient elements for plant growth, fruit development, and yield. The research and application of potassium nutrition diagnosis technology is the premise of scientific potassium management. However, potassium deficiency in tomato leaves, from vegetative to reproductive growth, is not easy to diagnose. To alleviate this problem, this paper proposes a suitable method of supplying potassium to tomatoes via a nutrient solution and diagnosing potassium abundance and deficiency through diagnosis methods based on ecological morphology, biological accumulation, and the photosynthetic characteristics of tomato plants. The relationship between the ecological morphology and biomass accumulation of tomatoes cultivated in the nutrient solution with potassium supply levels of 1, 4, 8, and 16 mmol/L is also discussed, and the potassium supply in the nutrient solution was studied 21 days after transplanting. The results showed that there was a significant quadratic correlation between the potassium supply in the nutrient solution and plant height and biomass accumulation, respectively. The most suitable level of potassium supply via the nutrient solution was deemed to be 10~13 mmol/L. However, if irreversible damage or severe stress to tomato plants has occurred because of potassium deficiency, there will be serious differences in the growth status of plants, and the diagnosis results will deviate greatly. In addition, the photosynthetic induction characteristics responding to the dark–light conversion of tomato leaves with potassium contents of 0.9%, 2.1%, 3.1%, and 3.3% cultivated with potassium supply amounts of 1, 4, 8, and 16 mmol/L in the nutrient solution were investigated. The results showed that tomato leaves with potassium contents of 3.1% and 3.3% had a more rapid response to dark–light conversion and higher first-order derivatives of net photosynthetic rate compared to those with potassium contents of 0.9% and 2.1%, but the first-order derivative of intercellular CO2 concentration showed an opposite trend. Additionally, a quadratic correlation between leaf potassium content and CO2 assimilation during 5 min of photosynthetic induction was established (R2 > 0.99). According to this correlation, the suitable leaf potassium content was estimated to be 2.3~2.7%, similar to that of tomatoes cultured in the nutrient solution with a 4~8 mmol/L potassium supply. Therefore, this method can realize the rapid, non-destructive, and real-time detection of potassium content in tomato leaves based on a portable photosynthetic measurement system by establishing the relationship between leaf potassium content and net CO2 assimilation during the photosynthetic induction period, therefore helping to avoid the irreversible damage caused by potassium deficiency at the later stages of plant cultivation and providing technical support for the precise fertilization of potassium in actual cultivation.

The pretension of building membrane structures may relax over its service lifetime, which may cause engineering failure under external loads. Therefore, the pretension of building membrane structures should be monitored or estimated regularly to compare the actual pretension to its design pretension and then to adopt some strengthening measures to mitigate future problems. Based on the geometrically nonlinear vibration of a rectangular orthotropic membrane structure, a nondestructive detection method for monitoring its pretension is developed in this article. This method is achieved by impacting a low-velocity pellet onto the membrane surface to generate vibration and detecting its response amplitude. Then the detected amplitude is converted into a pretension estimate via a derived formula. In addition, experiments for three kinds of conventional membrane material (Heytex H5573, Xing Yi Da, and ZZF 3010) were carried out according to the theoretical idea. The experimental results proved this method is feasible and verified the theoretical derivation is reasonable.