Search

BACKGROUND Food processing induces various modifications that affect the structure, physical and chemical properties of food products and hence the acceptance of the product by the consumer. In this work, the evolution of volatile components, 2-thiobarbituric acid reactive substances (TBARS), moisture content (MC) and microstructural changes of pork was investigated by hyperspectral (HSI) and confocal imaging (CLSM) techniques in synergy with gas chromatography-ion mobility spectrometry (GC-IMS). Models based on partial least squares regression (PLSR) were developed using the full HSI spectrum variables as well as optimum variables selected through a competitive adaptive reweighted sampling algorithm. RESULTS Prediction results for MC and TBARS using multiplicative scatter correction pre-processed spectra models demonstrated greater efficiency and predictability with determination coefficient of prediction of 0.928, 0.930 and root mean square error of prediction of 0.114, 1.002, respectively. Major structural changes were also observed during CLSM imaging, which were greatly pronounced in pork samples oven cooked for 15 and 20 h. These structural changes could be related to the denaturation of the major meat components, which could explain the loss of moisture and the formation of TBARS visualized from the HSI chemical distribution maps. GC-IMS identified 35 volatile components, including hexanal and pentanal, which are also known to have a higher lipid oxidation specificity. CONCLUSION The synergistic application of HSI, CLSM and GC-IMS enhanced data mining and interpretation and provided a convenient way for analyzing the chemical, structural and volatile changes occurring in meat during processing. © 2021 Society of Chemical Industry.

The study assessed the feasibility of merging data acquired from hyperspectral imaging (HSI) and electronic nose (e-nose) to develop a robust method for the rapid prediction of intramuscular fat (IMF) and peroxide value (PV) of pork meat affected by temperature and NaCl treatments. Multivariate calibration models for prediction of IMF and PV using median spectra features (MSF) and image texture features (ITF) from HSI data and mean signal values (MSV) from e-nose signals were established based on support vector machine regression (SVMR). Optimum wavelengths highly related to IMF and PV were selected from the MSF and ITF. Next, recurring optimum wavelengths from the two feature groups were manually obtained and merged to constitute “combined attribute features” (CAF) which yielded acceptable results with (Rc2 = 0.877, 0.891; RMSEC = 2.410, 1.109; Rp2 = 0.790, 0.858; RMSEP = 3.611, 2.013) respectively for IMF and PV. MSV yielded relatively low results with (Rc2 = 0.783, 0.877; RMSEC = 4.591, 0.653; Rp2 = 0.704, 0.797; RMSEP = 3.991, 0.760) respectively for IMF and PV. Finally, data fusion of CAF and MSV was performed which yielded relatively improved prediction results with (Rc2 = 0.936, 0.955; RMSEC = 1.209, 0.997; Rp2 = 0.895, 0.901; RMSEP = 2.099, 1.008) respectively for IMF and PV. The results obtained demonstrate that it is feasible to mutually integrate spectral and image features with volatile information to quantitatively monitor IMF and PV in processed pork meat.

In order to rapidly and nondestructively identify tea grades, fluorescence hyperspectral imaging (FHSI) technology was proposed in this paper. A total of 309 Tieguanyin tea samples with three different grades were collected and the fluorescence hyperspectral data was acquired by hyperspectrometer (400 to 1000 nm). The characteristic wavelengths were respectively selected by Bootstrapping Soft Shrinkage (BOSS), Variable Iterative Space Shrinkage Approach (VISSA) and Model Adaptive Space Shrinkage (MASS) algorithms. Then, Support Vector Machine (SVM) was applied to establishing the relationship between the characteristic peaks, the full spectra, three characteristic spectra and the labels of tea grades. The results showed that VISSA-SVM model had the best classification performance, but the model precision can still be improved. Thus, Artificial Bee Colony (ABC) algorithm was introduced to optimize the parameters of SVM model. The accuracy and Kappa coefficient of test set of VISSA-ABC-SVM model were improved to 97.436% and 0.962, respectively. Therefore, the combination of fluorescence hyperspectra with VISSA-ABC-SVM model can accurately identify the grade of Tieguanyin tea. PRACTICAL APPLICATION: The rapid and accurate nondestructive tea grade identification method contributes to the construction of the tea online grade detection system. FHSI technology can solve the shortcomings of the reported methods and improved the identification accuracy of tea grades. It can be applied to the rapid detection of tea quality by tea companies, tea market, tea farmers and other demanders.

BACKGROUND Various spectral profiles, including reflectance, absorbance, and Kubelka-Munk spectra, have been derived from hyperspectral images and used to develop multivariate models to evaluate changes in the quality of meat and meat products as a function of processing. However, none of these has the capacity to produce images of the structural changes often associated with processing. This study explored the feasibility of combining hyperspectral imaging (HSI) with confocal laser scanning microscopy (CLSM) to examine the impact of processing on microstructural changes and the evolution of moisture. Reflectance spectra features were obtained and transformed into absorbance and Kubelka-Munk spectra and their ability to predict moisture content using models established on partial least-squares regression were evaluated. RESULTS The partial least-squares regression model (full-band wavelength) dubbed Rs-MSC yielded the best result, with Rc2=0.967 , RMSEC = 0.127, Rcv2=0.949 , RMSECV = 0.418, Rp2=0.937 , RMSEP = 0.824. Next, a total of 16 optimum wavelengths were selected using the competitive adaptive reweighted sampling algorithm. These wavelengths also yielded good results for Rs-MSC, with Rc2=0.958 , RMSEC = 0.840, Rcv2=0.931 , RMSECV = 0.118, Rp2=0.926 , RMSEP = 0.121. Regarding moisture distribution and microstructure analysis, HSI and CLSM were able to reveal moisture content distribution and conformational differences in microstructure in the test samples. CONCLUSION Using HSI in synergy with CLSM may offer a reliable means for assessing both the chemical and structural changes that occur in other congener food products during processing. © 2020 Society of Chemical Industry.

Moisture content was an important indicator to measure quality of green tea. In order to detect moisture content in green tea effectively and accurately, nondestructive detection for moisture content in green tea based on dielectric technology was proposed in this paper. Inductance‐capacitance‐resistance (LCR) measuring instrument and coaxial‐line cylinder capacitor were used to collect dielectric data. The characteristic frequency points were extracted by successive projection algorithm (SPA) and variable iterative space shrinkage approach (VISSA). Support vector regression (SVR) was used to establish prediction models based on full frequency points and characteristic frequency points. The model results demonstrated that VISSA‐SVR model based on dielectric loss factor e″ performed best among all the prediction models, but the prediction accuracy was not enough, so the gray wolf optimization (GWO) algorithm was introduced to optimize the parameters (c and g) in SVR model. Furthermore, the best prediction performances for detecting moisture content in green tea was obtained, with the determination coefficient and root mean square errors (RMSEs) for prediction were 0.9695 and 0.0602, respectively. Therefore, dielectric technology combined with VISSA‐GWO‐SVR model is feasible for nondestructive determination of the moisture content in tea, which will provide a promising tool for the moisture content detection of other agricultural products. PRACTICAL APPLICATIONS: Well understanding moisture content in tea is great importance. The practical application of this paper is to develop a novel method for moisture content detection in tea using dielectric technology. Compared with traditional methods, dielectric technology can be used to detect moisture content in tea nondestructively and accurately. Characteristic frequency selection algorithms are used to remove the redundant information in the data, and optimization algorithm is used to improve the performance of the model. Thus, dielectric technology combined with the optimal model is considered the most promising method for detecting the moisture content in green tea.

Mid‐infrared diffuse reflectance spectroscopy was used to rapidly and nondestructively identify tea varieties together with the proposed possibilistic fuzzy c‐means (PFCM) clustering with a fuzzy covariance matrix. The mid‐infrared diffuse reflectance spectra of 96 tea samples with three different varieties (Emeishan Maofeng, Level 1, and Level 6 Leshan trimeresurus) were acquired using the FTIR‐7600 infrared spectrometer. First, multiplicative scatter correction was implemented to pretreat the spectral data. Second, principal component analysis was employed to compress the mid‐infrared diffuse reflectance spectral data after preprocessing. Third, linear discriminant analysis was utilized for extracting the identification information required by the fuzzy clustering algorithms. Ultimately, the fuzzy c‐means (FCM) clustering, the allied fuzzy c‐means (AFCM) clustering, the PFCM clustering, and the PFCM clustering with a fuzzy covariance matrix were used to cluster the processed spectral data, respectively. The highest identification accuracy of the PFCM clustering with a fuzzy covariance matrix reached at 100% compared with those of FCM (96.7%), AFCM (94.9%), PFCM (96.3%), and partial least squares discrimination analysis (PLS‐DA) algorithm (33.3%). It is sufficiently demonstrated that the mid‐infrared diffuse reflectance spectroscopy coupled with the PFCM clustering with a fuzzy covariance matrix was a valid method for identifying tea varieties. PRACTICAL APPLICATIONS: The variety of tea is vitally important to evaluate tea quality in the tea market. Mid‐infrared diffuse reflectance spectroscopy is deemed to be a convenient, rapid, accurate, and nondestructive detection technology in comparison with the traditional methods. In this article, the proposed PFCM clustering with a fuzzy covariance matrix coupled with mid‐infrared diffuse reflectance spectroscopy can be used to determine tea varieties quickly and correctly. The experimental results indicate the application potential in tea quality examination and fake tea products discrimination.

Due to the difference of raw materials and brewing technology, the quality and flavours of vinegar are different. Different kinds of vinegar have different functions and effects. Therefore, it is important to classify the vinegar varieties correctly. This work presented a new fuzzy feature extraction algorithm, called fuzzy Foley–Sammon transformation (FFST), and designed the electronic nose (E-nose) system for classifying vinegar varieties successfully. Principal component analysis (PCA) and standard normal variate (SNV) were used as the data preprocessing algorithms for the E-nose system. FFST, Foley–Sammon transformation (FST) and linear discriminant analysis (LDA) were used to extract discriminant information from E-nose data, respectively. Then, K nearest neighbor (KNN) served as a classifier for the classification of vinegar varieties. The highest identification accuracy rate was 96.92% by using the FFST and KNN. Therefore, the E-nose system combined with the FFST was an effective method to identify Chinese vinegar varieties and this method has wide application prospects.

In this study, the feasibility of the fluorescence hyperspectral imaging (FHSI) technology to detect the viability of soybean seeds was investigated. Viable and nonviable seed samples were obtained by artificial aging method. Hyperspectral images of samples were collected by the FHSI device and then the spectral data were collected. Characteristic wavelengths were respectively selected by three variable selection methods, eliminating a large number of redundant information irrelevant to the viability of soybean seeds. Support vector machine (SVM) models based on the full spectra and the optimal spectral data were developed to identify the viability of soybean seeds. To further improve the accuracy of the model, the adaptive boosting (AdaBoost) algorithm was used. The results showed that the accuracy of the calibration and validation sets in the CARS‐SVM‐AdaBoost model (22 characteristic wavelengths) reached 100%, indicating that the combination of FHSI technology and the optimization model can greatly improve the recognition accuracy. PRACTICAL APPLICATIONS: A rapid and accurate nondestructive identification method of viability of soybean seeds can contribute to the construction of the online seed viability detection system. FHSI technology has the advantages of high sensitivity and comprehensive analysis of sample information. Combined with the optimization model proposed in this paper, the recognition accuracy can be greatly improved. It can be applied to the online seed viability detection by seed companies, seed quality inspection departments, and soybean breeding units.

This study examined the feasibility of using hyperspectral imaging (HSI) to predict thiobarbituric acid reactive substances (TBARS) values as by‐products of lipid oxidation during traditional processing of dry‐cured meat. Using the spectral data of HSI and reference values of TBARS, models based on partial least square regression were tested. Prediction outcomes, Rₚ = 0.71, RMSEP = 1.10 and Rₚ = 0.77, RMSEP = 0.97 were obtained after preprocessing with first and second derivatives (second‐order polynomial), respectively. Preprocessing with standard normal variate, and multiplicative scatter correction yielded lower values, Rₚ = 0.64, RMSEP = 1.21 and Rₚ = 0.52, RMSEP = 1.40, respectively. Results obtained indicated that the HSI was capable of monitoring TBARS values as a lipid oxidation index during meat processing. Volatile fingerprint results for all processing phases were also obtained following analysis by gas chromatography (GC) coupled to ion mobility spectrometry (IMS) and a mass spectrometry (MS) detector. The MS was deployed to provide names and proportions of the volatile components while IMS was used to visualize the topographic maps of the individual volatile components. The GC‐IMS proved to be very versatile considering the little time it required to identify the volatile fingerprints of the various samples. PRACTICAL APPLICATIONS: The conventional methods for determining lipid oxidation in meat are highly destructive and time‐consuming and require the use of hazardous chemicals. This study evaluated the feasibility of the hyperspectral imaging (HSI) and gas chromatography coupled to ion mobility spectrometry (GC‐IMS) and a mass spectrometry detector to rapidly predict thiobarbituric acid reactive substances (TBARS) and analyze volatile components during lipid oxidation. The HSI was successful in predicting TBARS values as by‐products of oxidation. The results obtained proved that the methods presented can be useful for industries to simultaneously estimate various quality attributes of samples to save time, labor, and cost, which reflects economic importance. Additionally, the recurrence of hexanal during analysis volatile compounds means industries should be mindful of it as it acts as a marker for lipid oxidation in muscle food products. Finally, the topographic plots generated by the GC‐IMS are also very useful for easy differentiation of samples.

Saponin is one of the most important products of Tremella aurantialba fermentation. The purpose of this research is to develop a portable artificial visualization system for convenient and nondestructive monitoring the saponin content during the fermentation process of T. aurantialba in real time. This system consisted of a colorimetric sensor array (CSA) and image acquisition program. Support vector machine regression (SVR) model was utilized to establish the quantitative model for predicting saponin content. Principal component analysis was applied to reduce the input dimension of SVR model and predigest model complexity. The reaction time was optimized with the predicted results as the main concern. The results indicated good performance in the correlation between reference measurement and CSA predicted when the reaction time was 2 min. Additionally, genetic algorithm was used to select colorimetric sensors that resulted in the best‐fitted for models. The determination coefficient (Rₚ) was 0.8829 and the root‐mean‐square error of prediction was 1.7783 mg/mL in the testing set. The results validated that the designed system is feasible to quantify saponin content, and it could serve as a useful technology for quality control of submerged fermentation of T. aurantialba. PRACTICAL APPLICATIONS: The fermentation process is susceptive and even small changes can result in unacceptable final product quality. However, the classical analysis methods need off‐line extraction techniques and complex preparation of the sample and lengthy analysis time, which cannot satisfy the demand of in‐situ and rapid test. This study developed a portable artificial visualization system for convenient and rapid detection during the submerged fermentation of Tremella aurantialba, and tested its reliability and accuracy. The results obtained from this research sufficiently showed that the developed system has broad application prospects in the real‐time detection of the fermentation process.

In order to study the qualitative Vis–NIR spectra detection of cadmium stress in lettuce leaves, this article uses data‐level information fusion method coupled with VISSA‐GOA‐SVM algorithm for research and analysis. There were 140 pieces of lettuce leaves under four gradients of cadmium stress, as well as a total of 560 samples of lettuce were used to collect Vis–NIR spectral information. Wavelet transform (WT), first derivative (1st Der) and second derivative (2nd Der) were used to process original Vis–NIR spectral data, respectively. Besides, the data after different pretreatments will be fused in the data‐decision level and used as a new input layer. Principal component analysis (PCA), iteratively retaining informative variables (IRIV), and variable iterative space shrinkage approach (VISSA) were used to reduce the dimensionality of the data layer, respectively. Besides, support vector machine (SVM) was used to establish a classification model. The results showed that the RTD (initial fusion of three data input layers) combined with VISSA‐GOA‐SVM (VISSA combined with grasshopper optimization support vector machine) classification model was the best, and the accuracy of the calibration and prediction were 100% and 98.57%, respectively. Application of Vis–NIR hyperspectral imaging technique to detect cadmium stress gradient in lettuce leaves provided a new method for identifying different heavy metal residues in lettuce. PRACTICAL APPLICATIONS: It is of great significance to detect different cadmium stress levels through nondestructive testing. In order to effectively implement the rapid and nondestructive testing of lettuce leaves under different cadmium stress levels, Vis–NIR hyperspectral imaging technology coupled with data‐level fusion method was used in this article. In addition, VISSA‐GOA‐SVM model was established to detect cadmium stress level. It confirms that the Vis–NIR hyperspectral imaging technology is a feasible and effective method for discriminating different cadmium stress levels.

The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nose system can quickly detect different types of Chinese liquors according to their aromas. In this study, an e-nose system was designed to identify six types of Chinese liquors, and a novel feature extraction algorithm, called fuzzy discriminant principal component analysis (FDPCA), was developed for feature extraction from e-nose signals by combining discriminant principal component analysis (DPCA) and fuzzy set theory. In addition, principal component analysis (PCA), DPCA, K-nearest neighbor (KNN) classifier, leave-one-out (LOO) strategy and k-fold cross-validation (k = 5, 10, 20, 25) were employed in the e-nose system. The maximum classification accuracy of feature extraction for Chinese liquors was 98.378% using FDPCA, showing this algorithm to be extremely effective. The experimental results indicate that an e-nose system coupled with FDPCA is a feasible method for classifying Chinese liquors.

Objective. To pursue and reach the quality goal of zero defects and to further improve the quality of medical record management. Method. Apply the theory of zero defects to medical record management; the zero defect quality goal was gradually reached through total involvement in education, the construction of a professional team, improvement in the quality control system, quality standardization, removal of out-of-control quality problems, and continuous quality improvement. Results. After the implementation of zero-defect management, the accuracy of recovering medical records has increased from 97.1% to 100%; the accuracy of sorting discharge records has increased from 93.5% to 99.8%; the accuracy of medical record information processing has increased from 95.8% to 99.2%; and the accuracy of binding discharge records has increased from 99.8% to 100%; the on-the-shelf accuracy of medical records has increased from 99.9% to 100%; in two years, there has been no missing of medical records or filing ...

Freshness is one of the important qualities of fish on the priority list of consumers. During storage of fish, nutrient components such as protein, carbohydrates, and fats decompose to produce many volatile organic compounds (VOCs) which could be monitored using colorimetric sensor array (CSA). In this study, density functions theory (DFT) was applied to study the reaction between chromogenic sensitive materials and the characteristic gases of the fish during spoilage. Gas chromatography–mass spectrometry (GC–MS) was applied to confirm the characteristic gases of mackerels during storage. In the course of monitoring the VOCs during the various stages of spoilage, the relative content (%) of trimethylamine (TMA) was found to be increasing relative to the other volatile substances. Thus, TMA was selected as the characteristic gas for further analysis. Six kinds of metalloporphyrins and protoporphyrin were selected as chromogenic sensitive materials to simulate the reaction with TMA. The results demonstrated that MnTPP, NiTPP, and FeTPP have the best ability binding with TMA, while TPP has the worst ability binding with TMA, so metal porphyrins can be added when constructing colorimetric sensor array. PRACTICAL APPLICATIONS: Fish is an important food commodity worldwide and is popular with consumers because of its delicacy and nutrition. However, fish is highly perishable as a consequence of biochemical and microbial breakdown mechanisms. In this study, colorimetric sensor array was constructed to monitor quality changes during fish spoilage. GC–MS was applied to confirm the characteristic gases of mackerels during storage. DFT was applied to study the reaction between chromogenic sensitive materials and the characteristic gases of the fish during spoilage. The outcome of this study provided a theoretical basis for the construction of CSA. Based on the reaction mechanism, we can shorten time of screening sensitive materials. After the construction of CSA, we can simplify and shorten the detection time and labor of fish freshness detection.