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A thorough quantification of soil chemical properties is essential for assessing the engineering properties of forest soils for road design, construction, and maintenance. Here, we investigate the applicability of visible–near-infrared (Vis–NIR) spectroscopy in conjunction with advanced statistical analysis for estimation of soil chemical properties. Sixty forest soil samples were collected and analyzed for pH, electrical conductivity (EC), CaCO3, organic matter (OM), and cation exchange capacity (CEC) with established laboratory methods. The spectral measurements were performed with a Vis–NIR spectrometer within a range of 350–2,500 nm. To estimate abovementioned soil properties from reflectance spectra, advanced statistical techniques including partial least squares regression (PLSR), hybrid partial least squares and artificial neural networks (PLS–DI–ANN) models, hybrid partial least squares and adaptive neural fuzzy inference system (PLS–DI–ANFIS) models, as well as narrow band spectral indices were applied. The obtained results ​​indicate that the PLS–DI–ANFIS models show great potential for the estimation of pH, EC, OM, and CEC from reflectance spectra and their first derivatives, exhibiting higher R2 values and lower RMSE than the other investigated models. The estimation accuracy for CaCO3, however, was low for all applied methods. The results confirm that Vis–NIR spectroscopy may be applied as a rapid and cost-efficient alternative to standard chemical soil analysis techniques, aiding forest road design, construction, and maintenance.

In order to realize the rapid and non-destructive detection of fresh Cerasus Humilis’ (CH) classification, and promote the deep-processing of post-harvest fresh fruit and improve market competitiveness, this study proposed a nonlinear identification method based on genetic algorithm (GA) optimized back propagation (BP) neural network of different varieties of fresh CH fruit. “Nongda-4”, “Nongda-5”, and “Nongda-7” fresh CH fruit were selected as research objects to collect their visible/near-infrared spectral data dynamically. The original spectra were preprocessed by moving smoothing (MS) and standard normal variate (SNV) methods, for the characteristic wavelengths were extracted with four dimension-reducing methods, namely principal components analysis (PCA), competitive adaptive reweighed sampling (CARS), CARS-mean impact value (CARS-MIV), and random frog (RF) algorithm. Finally, the BP prediction models were established based on full-spectrum and characteristic wavelengths. At the same time, the GA optimization was used to optimize the initial weight and threshold of the BP neural network and compared with the partial least squares’ discrimination analysis (PLS-DA) linear model. Through comparing the MS (7)+SNV was proved to be the best preprocessing method, the CARS-MIV-GA-BP model had the best discriminant accuracy, the prediction set accuracy was 98.76%, of which the variety “Nongda-4” and “Nongda-5” recognition rate were 100%, the variety “Nongda-7” recognition rate was 96.29%. The results show that the GA can effectively optimize the initial weights and threshold randomization of the BP neural network, improve the discrimination accuracy of CH varieties, and the CARS-MIV algorithm can effectively reduce the number of input nodes of the BP neural network model, simplify the structure of BP neural network. This study provides a new theoretical basis for the detection of fresh CH fruit classification.

The objective of this study was to study the ability of the VIS-NIR spectroscopy to classify the pure and adulterated stingless bee honey across the wavelength range of 450– 969 nm using an optical spectrometer. The physicochemical properties such as soluble solid content (SSC) and moisture content (refractive index, RI) of pure and adulterated honey has also been investigated using a refractometer. The result showed that pure stingless bee honey has the highest transmittance rate, SSC and RI value compared to adulterated honey. There are significant differences (P < 0.0001) in the transmittance rate, SSC and RI of stingless bee honey over five different types of treatments. The results also showed that VIS-NIR data were good in classifying the samples into different treatments with 99.33% accuracy rate. About thirty-four wavelengths were found to be the most significant to discriminate the different treatments by principal component analysis (PCA) and linear discriminant analysis (LDA) techniques.

Deoxynivalenol (DON) contamination of wheat grains is a serious problem in China, and it is necessary to remove contaminated wheat before it enters the consumer market. In this study, visible-near infrared (Vis-NIR) spectroscopy and computer vision techniques were combined to simulate online discrimination between normal and DON-contaminated wheat grains. Naturally growing wheat samples were collected from several of the main wheat-producing areas in China, the reference DON contents were measured by using liquid chromatography serial triple quadrupole mass spectrometer (LC-MS), and then wheat samples were divided into two categories according to the national standard of 1 mg kg−1. The characteristic spectral variables, colour and texture features were extracted and integrated for chemometric analysis. Principal component analysis based on fusion features indicated better clustering than with just spectral features. Subsequently, linear discriminant analysis modelling based on spectra and texture features achieved the best discrimination with an accuracy of 95.06% and 91.36% for calibration and validation sets respectively, which was 5% higher than with just spectral features, and the false positive rates (FPR) were the lowest: 3.41% and 10.42% for calibration and validation sets respectively. The internal scanning results of whole wheat flour indicated that the higher the content of DON, the looser the binding of starch granules, which could cause the textural change of wheat grains. The research showed that Vis-NIR spectroscopy combined with computer vision has the potential to be used in the non-destructive and online detection of DON-contaminated wheat grains; further study on the interference from complex environments is still need for actual online detection.

The purpose of this study was to estimate local calibration models for 30 chemical and 4 physical soil properties, based on visible and near-infrared subsurface soil reflectance spectra (VNIR-SRS)....

This study aimed to predict soil properties using visible–near infrared (VIS-NIR) spectroscopy combined with partial least square regression (PLSR) modeling. Special emphasis was given to evaluatin...

The overuse of nitrogenous fertilizers leads to an increase in the nitrate content of green leafy vegetables. Consumption of food with excess nitrate is not advisable because it results in human ailment. In this study, spinach leaves were harvested from plants grown under nine varying (0 to 400 kg/ha) nitrogenous fertilizer doses. A total of 261 samples were used to predict the nitrate content in spinach leaves using Vis-NIR (350 to 2,500 nm). The nitrate content was measured destructively using the ion-selective conductive method. Partial least square (PLS) regression models were developed using whole spectra and featured wavelengths. Spectral data were pre-processed using different spectral pre-processing techniques such as Savitzky-Golay (SG) derivative, standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction, and detrending. The predictive accuracy of the PLS model had improved after pre-processing of spectral data with MSC (RPDCV = 1.767; SECV = 545.745; biasCV = -3.107; slopeCV = 0.698) and SNV (RPDCV = 1.768; SECV = 545.337; biasCV = -3.201; slopeCV = 0.698) technique, but this was not significant (P < 0.05) as compared with raw spectral data (RPDCV = 1.679; SECV = 572.669; biasCV = -7.046; slopeCV = 0.687). The effective wavelengths for measurement nitrate content in spinach leaves were identified as 558, 706, 780, 1,000, and 1,420 nm. The performance of PLS model developed with effective wavelengths also had good prediction accuracy (RPDCV = 1.482; SECV = 648.672; biasCV = -3.805; slopeCV = 0.565) but significantly lower than the performance of model developed with full spectral data. The overall results of this study suggest that Vis-NIR spectroscopy can be an important tool and has great potential for the rapid and nondestructive assessment of nitrate content in harvested spinach, with a view to ascertain the suitability of the harvest for food uses. PRACTICAL APPLICATION: Better production and brighter color of leafy vegetable drive the farming community to overuse nitrogenous fertilizer. This has resulted in higher nitrate content in vegetables. It has been widely reported that consumption of these vegetables has carcinogenic effects on human beings. The prediction of nitrate content in leafy vegetables by traditional methods is time-consuming (30 min, including sample preparation time), destructive, and tedious; moreover, it cannot be used for inline applications. This study reports spectroscopy-based rapid (

Increasing consumption of green tea is attributed to the beneficial effects of its constituents, especially polyphenols, on human health, which can be varied during leaf processing. Processing technology has the most important effect on green tea quality. This study investigated the system dynamics of eight catechins, gallic acid, and caffeine in the processing of two varieties of tea, from fresh leaves to finished tea. It was found that complex biochemical changes can occur through hydrolysis under different humidity and heating conditions during the tea processing. This process had a significant effect on catechin composition in the finished tea. The potential application of visible and near‐infrared (Vis‐NIR) spectroscopy for fast monitoring polyphenol and caffeine contents in tea leaves during the processing procedure has been investigated. It was found that a combination of PCA (principal component analysis) and Vis‐NIR spectroscopy can successfully classify the two varieties of tea samples and the five tea processing procedures, while quantitative determination of the constituents was realized by combined regression analysis and Vis‐NIR spectra. Furthermore, successive projections algorithm (SPA) was proposed to extract and optimize spectral variables that reflected the molecular characteristics of the constituents for the development of determination models. Modeling results showed that the models had good predictability and robustness based on the extracted spectral characteristics. The coefficients of determination for all calibration sets and prediction sets were higher than 0.862 and 0.834, respectively, which indicated high capability of Vis‐NIR spectroscopy for the determination of the constituents during the leaf processing. Meanwhile, this analytical method could quickly monitor quality characteristics and provide feedback for real‐time controlling of tea processing machines. Furthermore, the study on complex biochemical changes that occurred during the tea processing would provide a theoretical basis for improving the content of quality components and effective controlling processes., Green tea polyphenols and caffeine are affected by variety and leaf processing step. Vis‐NIR spectra can distinguish different varieties and processing steps. This approach could quickly detect quality characteristics during leaf processing.

Successful estimation of soil total nitrogen (TN) content by visible and near-infrared (Vis-NIR) reflectance spectroscopy depends on selecting appropriate variable selection techniques and multivar...

The appearance of white striations over the breast of chicken and turkey meat is considered as a recent emerging and growing problem. The aim of this research is to investigate the ability of visible-near infra-red (VIS/NIR) spectroscopy to predict the quality traits of different levels of white striping (thickness of white striations, moderate < 1 mm and severe ≥ 1 mm) defects in turkey breast muscle. Accordingly, 36 turkey breast fillets affected by different level of white striping defects (normal, moderate and severe) were selected from 20-wk old tom turkeys. Colour traits (L*, a* and b*), pH, marinade uptake, drip loss, cooking loss, and chemical composition (moisture, fat, protein and ash) have been evaluated. Our findings showed that prediction models using partial least squares (PLS) were good for colour traits (a* for example; RPD values were 3.22 and 1.27, R2P were 0.91 and 0.57 while RER values were 11.8 and 3.12) , pH (RPD values were 5.00 and 0.01, R2P were 0.95 and 0.07 while RER values were –1.00 and 15.50), and chemical composition (protein content for instance, the prediction values were as the following: RPD values were 1.93 and 0.79, R2P were 0.80 and 0.34 and then RER were 8.48 and 3.80) in particular for normal and severe white striped meat respectively. In conclusion, the results of this research showed that VIS/NIR spectroscopy prediction models were satisfactory to predict the quality traits in the majority of cases.HIGHLIGHTS White striping is a recent muscle abnormality that affects adversely poultry meat. Visible-near infra-red (VIS/NIR) spectroscopy was used to predict the quality traits of turkey breast meat affected by white striping. Partial least squares (PLS) was good indicator to predict several quality traits of normal meat.

Traditional soil organic carbon density (SOCD) measurement is time-consuming and costly, from field sampling to laboratory analysis. However, visible to near-infrared reflectance (vis-NIR) spectroscopy can rapidly estimate SOCD and thereby permit rapid, inexpensive measurements that support environmental management. Our aim was to explore the relationship between SOCD and vis-NIR spectra, with the goal of establishing a spectral SOCD estimation method. In this study, we sampled soils throughout northern China’s agro-pastoral ecotone and performed SOCD and spectral measurements. After pre-processing the data, we transformed the spectral reflectance into seven forms: the reciprocal logarithm, first-order differential, second-order differential, logarithmic first-order differential, logarithmic second-order differential, reciprocal first-order differential (RFOD), and reciprocal second-order differential. We then explored the relationship between SOCD and these indexes. We also analyzed spectral SOCD estimation models based on stepwise multiple linear regression and partial least-squares regression. We found that the spectral reflectance decreased with increasing SOCD, and the most sensitive bands for SOCD were between 745 and 840 nm. RFOD greatly improved estimation accuracy. The performance of a stepwise multiple linear regression estimation model based on RFOD provided the best fit (R2 = 0.77) and the lowest root-mean-square error (0.75 kg C m−2), and the best ratio of percent deviation (2.11). Our results suggest a high potential for fast and reliable estimation of SOCD using spectral techniques and provides a theoretical basis and technical support for rapid monitoring of SOCD in northern China’s agro-pastoral ecotone.

The traditional candling methods are very hard to discriminate the infertile chicken eggs from the fertile ones before hatching on the poultry farm. The purpose of this study was to detect infertil...

Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (n = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.

This study aimed to evaluate the feasibility of mid-infrared (MIR; 5000–900 cm−1) and visible/near-infrared (Vis/NIR; 400–2500 nm) spectroscopy to discriminate organic (ORG) from conventional (CONV) bulk milk. Samples (n = 225) from 24 farms (ORG, n = 12; CONV, n = 12) located in the same area, mainly rearing Holstein-Friesian cows, under similar management conditions, except for ORG livestock spending a period of time on pasture, were collected from September 2019 to August 2020. Chemical composition of the lactation ration was similar between groups. Mid-infrared and Vis/NIR spectrum of each sample were collected. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were done using R software. For the PLS-DA, records were divided in a train set (8 farms/group) and a test set (4 farms/group), and only wavelengths with VIP >1 were retained. The PCA was not able to discriminate both groups. The PLS-DA revealed an accuracy of the model in the test set of 54.1% and 62.9%, for MIR and Vis/NIR, respectively. In conclusion, both infrared regions performed similarly, and the moderate accuracy of the PLS-DA could be related to the similarity of the selected farms between both categories.Highlights Infrared spectra from bulk milk samples of organic and conventional farms were similar. Low accuracy using partial least squares-discriminant analysis. Both MIR and Vis/NIR infrared regions performed similarly.

The non-destructive visible/near-infrared (VIS/NIR) spectroscopy is a promising technique in determining the quality of agricultural commodities. Therefore, this study aimed to examine the ability of VIS/NIR spectroscopy (550–1100 nm) to distinguish between three different varieties of tomato (i.e., Ekram, Harver and Izmer), as well as to predict the quality parameters of tomato, such as soluble solids content (SSC), titratable acidity (TA), taste (SSC/TA) and firmness. Ninety intact samples from three tomato varieties were used. These samples were examined using VIS/NIR spectroscopy and quality parameters were also measured using traditional methods. Principal component analysis (PCA) and partial least square (PLS) were carried out. The results of PCA showed the ability of VIS/NIR spectroscopy to distinguish between the three varieties, where two PCs explained about 99% of the total variance in both calibration and validation sets. Moreover, PLS showed the possibility of modelling quality parameters. The correlation coefficient (R2) and the ratio of performance deviation (RPD) for all quality parameters (except for firmness) were found to be higher than 0.85 and 2.5, respectively. Thus, these results indicate that the VIS/NIR spectroscopy can be used to discriminate between different varieties of tomato and predict their quality parameters.

Visible and near infrared (Vis-NIR) spectroscopy is a rapid, accurate, cost-effective, and nondestructive alternative method for soil analysis. The aim of this study was to examine the potential of...

Designer hen shell eggs, with enhanced or reduced specific components, are used as part of a healthy diet, and such designer eggs must meet certain standards. A fast, nondestructive technology is required in order to ensure that these standards are met. In this study, we investigated the nondestructive estimation of intravitelline vitamin E and cholesterol concentrations in hen shell eggs using visible and near-infrared (VIS/NIR) spectroscopy. The values estimated using partial least-squares regression (PLSR) models were highly correlated with vitamin E concentration measurements by high-performance liquid chromatography and cholesterol concentration measurements by enzymatic analysis. Wavelength selection with the Martens uncertainty test successfully removed unimportant variables and enhanced the models' accuracy and reliability. However, spectral measurements of separated egg yolks with artificially controlled vitamin E and cholesterol concentrations did not afford accurate predictive models. This result for separated yolks, together with band assignments of PLSR models for hen shell eggs, suggested that VIS/NIR spectroscopy can indirectly estimate vitamin E and cholesterol concentrations in hen shell eggs by using information about other components.

HighlightsA custom-designed online Vis/NIR spectroscopy system was used for real-time detection of watercore in apples.Watercore severity index (WSI) was applied for watercore severity assessment.Higher than 95.0% accuracy was obtained for total samples in classifying sound apples from watercore groups using kNN, BPNN, SVM, and 1D CNN at a detection speed of 3 apples s-1.Linear kernel SVM achieved the best classification accuracy of 96% for samples in the prediction set.Abstract. Watercore, an internal physiological disorder affecting apples, can be characterized by water-soaked, glassy regions near the fruit core. It is used as an indicator of full ripeness, storage suitability, and price of apples in many countries. Therefore, fast and non-destructive detection of watercore plays an important role in improving the commercial value of apples and reducing postharvest costs. In this study, an online visible/near-infrared (Vis/NIR) spectroscopy system was proposed for real-time detection of watercore in ‘Fuji’ apples (Malus pumila Mill.). A total of 318 samples harvested during harvest season in the same orchard were analyzed for both watercore severity index (WSI) and soluble solids content (SSC). According to the USDA watercore classification standard, all samples were classified into one of four classes (sound, slight, moderate, or severe) based on the affected area of watercore. Results showed that, although there was a positive correlation between spectral intensity and affected area of watercore, no significant relationship between affected area size and SSC could be obtained by Pearson test (correlation coefficient ~0.094). Generally, >95.0% accuracy was obtained for total samples at a detection speed of 3 apples s-1 in classifying sound from watercore groups using k-nearest neighbors (kNN) algorithm, back-propagation neural network (BPNN), support vector machine (SVM) classification, and one-dimensional convolutional neural network (1D-CNN). The best classification result was achieved by linear kernel SVM, with an accuracy of 96% for total samples. These classification algorithms showed preliminary feasibility for online screening of apples with watercore using Vis/NIR spectroscopy in industrial applications. Keywords: Apple watercore, Machine learning, Online detection, Vis/NIR spectroscopy, Watercore severity index.

Environmental risk of heavy metals (HMs) in soil is commonly assessed by the different risk screening values of HMs under different pH based on soil environmental quality standards. To explore and establish a reliable, rapid and cost-effective method for detailed soil environmental quality survey with high-density sampling in the large-scale area is of significance for theoretical and practical research. In present study, using data from Yunnan Province, China, rapid analysis of soil HMs were conducted via portable X-ray fluorescence (PXRF) spectrometry, and that of soil pH was estimated by applying PXRF and visible near-infrared reflectance (Vis-NIR) spectroscopy data to partial least-squares regression (PLSR) and support vector machine regression (SVMR). Then we compared soil HM contamination grades calculated by conventional laboratory analysis data with those of rapid analysis data. It was found that soil HMs (i.e., As, Pb, Cu, and Zn) were successfully estimated by PXRF with high coefficient of determination (R2) above 0.97 (P

The objective of this study was to compare transmission and reflectance modes of visible (VIS)/near infrared (NIR) spectroscopy for their ability to nondestructively determination of key pomegranate quality attributes such as total soluble solid content (TSS), pH and firmness. Partial least squares (PLS) regression was used to develop calibration models. The reflectance mode predicted TSS with r = 0.95, RMSEC = 0.22 °Brix and RPD = 6.7 °Brix by calibration models. These parameters for the validation models were found to be: r = 0.94, RMSEP = 0.21 °Brix and RPD = 6.72 °Brix. The pH was predicted with r = 0.85, RMSEC = 0.068 °Brix and RPD = 4.58 °Brix for calibration set and r = 0.86, RMSEP = 0.069 °Brix and RPD = 4.43 °Brix for validation sets by reflectance mode. The results indicated that it was possible to use both the transmission and reflectance modes to develop a system in determination of the internal attributes of pomegranate fruit. However, reflectance mode spectra provided more accurate assessment of TSS, pH and firmness.

Hatching window and the gender of hatching eggs were investigated in relation to the yolk content and yolk ratio (yolk to egg ratio) of the eggs. To quantify the amount of yolk noninvasively, a regression model from absorbance spectra (600–900 nm) was developed using known yolk content. This model was then used to determine the yolk content subsequently the yolk to albumen ratio of the hatching eggs. This yolk information of hatching egg was then correlated with hatching time and the gender of the egg. The results showed a significant correlation between estimated yolk-albumen ratio and hatching time, but no significant correlations were obtained with the gender of the eggs.

Fruits are one of the sources of nutrition needed for health. Fruit quality is generally assessed by physical and chemical properties. Measurement of fruit internal quality is usually done by destructive techniques. Ultraviolet, visible and near-infrared (UV-Vis-NIR) spec-troscopy is a non-destructive technique to measure fruit quality. This technique can rapidly measure the fruit quality, the measured fruit still remains intact, and can be marketed. Besides, UV-Vis-NIR spectrosco-py can also be used to classify fruits. The study aimed to classify var-ious types of fruits using UV-Vis-NIR spectroscopy with wavelengths of 300-1041 nm and Principal Component Analysis (PCA). First de-rivative savitzky-golay with 9 smoothing points (dg1) and multiplica-tive scatter correction (MSC) were applied to correct the spectra. The results showed that the use of uv-vis-nir spectroscopy and PCA com-bined with spectra pre-treatment of the MSC method were able to clas-sify various types of fruits with 100% success rate in all fruit samples including sapodilla, ridge gourd, mango, guava, apple and zucchini.

Soil fertility is typically assessed by chemical analysis, which is expensive and time-consuming, and hence impractical for site-specific fertilizer management. Visible and near infrared (Vis–NIR) spectroscopy has been used for determining soil properties and chemically extractable plant nutrients. However, the suitability of Vis–NIR for accurate assessment of nutrient availability to plants has not yet been fully explored. In this work, we examined the accuracy of this technique as a new nutrient availability index, and in the case of P as a proxy for plant-available P. To this end, total plant-available P in soil was quantified in a P-depletion experiment with crops, and the availability of Ca, Mg, K, and Fe was assessed by chemical extraction. Vis–NIR spectroscopy allowed us to accurately estimate plant-available P, which depends not only on soil factors but also on the crop performance to take up P. Vis–NIR spectroscopy proved effective in identifying P, Ca, Mg, K, and Fe responsive sites. Precise estimation of plant-available P was a result of accurately predicting soil properties governing P availability to plants by Vis–NIR spectroscopy. In addition, this technique provided accurate predictions of soil properties influencing the dynamics of applied P and K fertilizer, which can be useful to adapt fertilization practices to soil properties. Vis–NIR spectroscopy can therefore enable a qualitative leap to cost-effective integral assessment of soil fertility by providing accurate predictions of soil functioning rather than mere estimates of availability indices, thereby facilitating more sustainable use of resources in agriculture.

Aim of study: A portable VIS/NIR spectrometer and chemometric techniques were combined to identify bitter pit (BP) in Golden apples. Area of study: Worldwide Material and methods: Three different classification algorithms – linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support-vector machine (SVM) –were used in two experiments. In experiment #1, VIS/NIR measurements were carried out at postharvest on apples previously classified according to 3 classes (class 1: non-BP; class 2: slight symptoms; class 3: severe symptoms). In experiment #2, VIS/NIR measurements were carried out on healthy apples collected before harvest to determinate the capacity of the classification algorithms for detecting BP prior to the appearance of symptoms. Main results: In the experiement #1, VIS/NIR spectroscopy showed great potential in pitted apples detection with visibly symptoms (accuracies of 75–81%). The linear classifier LDA performed better than the ultivariate non-linear QDA and SVM classifiers in discriminating between healthy and bitter pitted apples. In the experiment #2, the accuracy to predict bitter pit prior to the appearance of visible symptoms decreased to 44–57%. Research highlights: The identification of apples with bitter pit through VIS/NIR spectroscopy may be due to chlorophyll degradation and/or changes in intercellular water in fruit tissue. info:eu-repo/semantics/publishedVersion

Soil contamination by potentially toxic elements (PTEs) is intensifying under increasing industrialization. Thus, the ability to efficiently delineate contaminated sites is crucial. Visible–near infrared (vis–NIR: 350–2500 nm) and X-ray fluorescence (XRF: 0.02–41.08 keV) spectroscopic techniques have attracted tremendous attention for the assessment of PTEs. Recently, the application of fused vis–NIR and XRF spectroscopy, which is based on the complementary effect of data fusion, is also increasing. Moreover, different data manipulation methods, including feature selection approaches, affect the prediction performance. This study investigated the feasibility of using single and fused vis–NIR and XRF spectra while exploring feature selection algorithms for the assessment of key soil PTEs. The soil samples were collected from one of the most heavily polluted areas of the Czech Republic and scanned using laboratory vis–NIR and XRF spectrometers. Univariate filter (UF) and genetic algorithm (GA) were used to select the bands of greater importance for the PTE prediction. Support vector machine (SVM) was then used to train the models using the full-range and feature-selected spectra of single sensors and their fusion. It was found that XRF spectra alone (primarily GA-selected) performed better than single vis–NIR and fused spectral data for predictions of PTEs. Moreover, the prediction models that were derived from the fused data set (particularly the GA-selected) enhanced the models’ accuracies as compared with the single vis–NIR spectra. In general, the results suggest that the GA-selected spectra obtained from the single XRF spectrometer (for As and Pb) and from the fusion of vis–NIR and XRF (for Pb) are promising for accurate quantitative estimation detection of the mentioned PTEs.

High-resolution, field-scale soil organic carbon (SOC) mapping in croplands is crucial for effective and precise agricultural management. Recent developments in unmanned aerial vehicles (UAVs) combined with miniaturized visible–near infrared spectrometers have enabled the rapid and low-cost field-scale SOC mapping. However, a field-specific spectrotransfer model is often needed for such UAV-based hyperspectral measurements, implying local sampling and model development are still required, and this hampers the widespread application of UAV-based methods. In this study, we aim to test to what extent SOC prediction models derived from an existing regional soil spectral library (SSL) can be applied to UAV-based hyperspectral data, without the need for additional field sampling. To this end, an UAV survey was conducted over a bare cropland within the Belgian Loam Belt for field-scale SOC mapping. We evaluated two calibration approaches, one based on local sampling and model development, and one where we capitalized on an existing (laboratory-based) regional SSL. For the local calibration approach, we obtained a good prediction performance with RMSE of 0.57 g kg−1 and RPIQ of 2.35. For the regional model, a spectral alignment procedure was needed to resolve the discrepancy between UAV- and laboratory-based measurements. This resulted in a fair SOC prediction accuracy with RMSE of 0.93 g kg−1 and RPIQ of 1.45. The comparison of SOC maps derived from the two approaches, along with an external validation showed a high consistency, indicating that UAV-based spectral measurements, in combination with SSLs have the potential to improve the efficiency of high-resolution SOC mapping.

Our work aimed to evaluate the use of visible–near-infrared (Vis–NIR) spectroscopy for predicting the production of leaf dry mass (LDM), as well as macro- and micronutrients contents of soybean leaves grown after application of limestone-mining coproducts. The treatments were arranged within a triple factorial scheme (6 × 2 × 2 + 2) and placed into pots in a greenhouse. We evaluated the following factors: type of input (limestone-mining coproducts), input particle size (filler and powder), and soil class (Arenosol and Ferralsol). After inputs incubation, the soybean was sown. Then, 42 days after sowing, we collected the foliar spectra, as well as leaves, for further analysis of the contents of macro- and micronutrients in leaves and production of LDM. We managed to adjust models at the stage of prediction with R2p > 0.50 and RPDp > 1.50 for the variables LDM, P, K, Mg, S, and Zn, with emphasis on the first four, which presented R2p above 0.65. Therefore, we conclude that Vis–NIR spectroscopy has a potential for predicting LDM and the nutrients contents of soybean subjected to the application of limestone-mining coproducts, with advantages such as speed, low cost, and no use of reagents that are toxic to the environment.

Current methods for detecting aflatoxin contamination of agricultural and food commodities are generally based on wet chemical analyses, which are time-consuming, destructive to test samples, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening and on-site detection. In this study, we utilized visible–near-infrared (Vis-NIR) spectroscopy over the spectral range of 400–2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B1 (AFB1). The artificially contaminated samples were prepared by dropping known amounts of aflatoxin standard dissolved in 50:50 (v/v) methanol/water onto peanut kernel surface to achieve different contamination levels. The partial least squares discriminant analysis (PLS-DA) models established using the full spectra over different ranges achieved good prediction results. The best overall accuracy of 88.57% and 92.86% were obtained using the full spectra when taking 20 and 100 parts per billion (ppb), respectively, as the classification threshold. The random frog (RF) algorithm was used to find the optimal characteristic wavelengths for identifying the surface AFB1-contamination of peanut kernels. Using the optimal spectral variables determined by the RF algorithm, the simplified RF-PLS-DA classification models were established. The better RF-PLS-DA models attained the overall accuracies of 90.00% and 94.29% with the 20 ppb and 100 ppb thresholds, respectively, which were improved compared to using the full spectral variables. Compared to using the full spectral variables, the employed spectral variables of the simplified RF-PLS-DA models were decreased by at least 94.82%. The present study demonstrated that the Vis-NIR spectroscopic technique combined with appropriate chemometric methods could be useful in identifying AFB1 contamination of peanut kernels.

Different spectroscopy methods such as visible near-infrared (Vis-NIR) spectroscopy have proven to provide useful information on soil physical and chemical properties. The majority of previous stud...

22 Páginas; 5 Figuras; 3 Tablas, Food nutritional labeling is compulsory in the European Union since 13 December 2016. The olive oil fatty acid composition shows high variation depending mainly on the variety. Thus, olive oil nutritional labeling is problematic for the industry. Besides, the analysis of all batches of olive oil using the official methods is expensive. Therefore, the olive oil industry is seriously concerned about solutions for nutritional labeling. In this study, a new rapid technique to measure the nutrients for the olive oil nutritional labeling, is assessed. A novel partial least squares (PLS) calibration model using log-ratio coordinates has been formulated and successfully tested for predicting the percentages of monounsaturated, saturated, and polyunsaturated fatty acids based on visible and near infrared spectroscopy. The model provided accuracy suitable for labeling, under the rules in force in the European Union. The error was generally much lower than the tolerance., J. A. Cayuela expresses gratitude to the European Regional Development Fund, aswell to the Ministry of Economy and Competitiveness of Spain for funding parts of this study, which was developed within the project Recupera 2020 1.4.4., and to the Spanish Council for Scientific Research, for co-funding the grant 20137R065 corresponding to this project. J. Palarea-Albaladejo was partly supported by the Scottish Government's Rural and Environment Science and Analytical Services Division (RESAS) and by the Spanish Ministry of Economy and Competitiveness under the project CODA-RETOS MTM2015-65016-C2-1(2)-R.

This research was performed to study calibration model transfer between White Leghorns eggs and Bantam eggs for prediction of egg freshness by visible near infrared (VIS-NIR) spectroscopy. Transmission spectra of the two varieties were acquired in the equatorial region of the eggs. And albumen pH as the freshness evaluating parameter was measured using traditional destructive methods. After outliers were eliminated by Mahalanobis distance combined with principal component analysis (PCA), partial least squares regression (PLSR) with different preprocessing methods was used to develop prediction models. Global updating, direct standardization (DS) and slope/bias correction (SBC) were evaluated to transfer calibration models from one variety to another. The Kennard-Stone (KS) algorithm was used to select standardization samples. White Leghorns eggs and Bantam eggs as the master variety in turn were compared to find superior master variety. Application of the slope/bias correction (SBC) algorithm obtained the best prediction results of albumen pH. And the better slope/bias correction (SBC) transfer performance with a rp of 0.908 and a RMSEP of 0.133 was found when Bantam eggs were as the superior master variety.

Methods were studied to predict the N, P, and K contents in sugarcane leaves quickly and accurately at the seedling, tillering, and elongation stages from leaf spectral reflectance. A total of 117 valid leaf samples were used to obtain leaf spectral reflectance with an indoor VIS-NIR spectrophotometer. Using the spectral data processed by CARS-PCA as an independent variable, a six-fold cross-validated PLS model for N, P, and K contents was established. The R2 values of the CARS-PCA-PLS models for N, P, and K prediction were 0.859, 0.677, and 0.932, respectively. Correlation analysis of the predicted N, P, and K contents was performed to explore the interaction effects between N, P, and K. To simulate the interaction effects among the three nutrients, 19 factors were assumed, including possible linear, quadratic, and cubic relationships between N, P, and K, and multi-factor cubic polynomial PLS and MLR regression models were established from those factors. In the modified MLR models, the determinants of N, P, and K were 0.891, 0.802, and 0.944, respectively, which improved the performance of the models by 3.7%, 18.5%, and 1.3%, respectively, compared with the CARS-PCA-PLS models, which were based on the spectral reflectance data. The results showed that application of VIS-NIR spectra combined with interaction effects between the nutrients could effectively predict the N, P, and K contents in the early and middle growth stages of sugarcane.HighlightsCompetitive adaptive reweighted sampling (CARS) was adopted to select wavebands for nutrient prediction.N, P, and K interaction effects were simulated with 19 factors, including linear, quadratic, and cubic relationships.The interaction factors were used in multiple linear regression models, and improved prediction was achieved. Keywords: CARS-PCA, Interaction effect, NPK, Sugarcane, VIS-NIR spectroscopy.

Visible/Near-infrared (Vis/NIR) spectroscopy at a range of 450–1000 nm was used to predict the values of three qualitative variables (starch, reducing sugar, and moisture content) on 200 potato tubers from 2 potato genotypes (‘Agria’ and ‘Clone 397009–8’) stored in both traditional and cold storages. After spectroscopy measurements, these variables were measured using reference methods. Then, Partial Least Square (PLS) models were developed. To evaluate developed models, Root Mean Square Error of calibration and cross validation (RMSEC and RMSECV), as well as coefficient of determination for calibration and cross validation (R 2 C and R 2 CV), and Residual Predictive Deviation (RPD) were used. The best prediction belonged to reducing sugar with statistical values of R 2 C = 0.99, R 2 CV = 0.98, RMSEC = 0.029, RMSECV = 0.037, and RPD = 7.57 in ‘Clone’ genotype stored under cold storage. The weakest prediction was related to moisture content with statistical values of R 2 C = 0.93, R 2 CV = 0.92, RMSEC = 0.268, RMSECV = 0.279, and RPD = 6.45 in stored ‘Clone’ genotypes under cold storage. Results of the study showed that, Vis/NIR spectroscopy as a non-destructive, fast, and reliable technique can be used for prediction of inner compositions of stored potatoes.

Adulteration of desiccated coconut powder (DCP) with coconut milk residue (CMR) is an emerging problem in the coconut processing industry. Consumers and industries are looking for a simple non-destructive device to measure the purity of DCP. vis-NIR (350–2500 nm) spectroscopy along with the chemometric techniques have been used to assess the purity of DCP. In this study, DCP was adulterated with CMR at different levels such as 0 (pure DCP), 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100% (pure CMR). Partial least squares regression (PLSR) models were developed using whole spectral data and selected wavelengths. The spectral data were pre-processed using different techniques such as raw, MSC + SNV, SG-smoothing, and detrending. The R2 of the models constructed with the pre-processed spectral data was higher than 0.950, irrespective of pre-processing technique. Pre-processing of spectral data does not have a significant effect on model performance when compared with the model developed using raw spectral data (R2P = 0.973; SEP = 9.681; RPDP = 9.381; RERP = 10.389), but the prediction accuracy was decreased. The wavelengths 653, 933, 1189, 1383, 1444, 1670, and 1911 nm were selected as the featured wavelengths for quantification of adulteration level in DCP. No significant difference in statistical results was observed between the PLSR model developed with selected wavelengths (R2P = 0.869; SEP = 11.701; RPDP = 9.381; RERP = 8.595) and the PLSR model for whole spectral data. The developed model can be used to predict the level of adulteration in DCP if the adulterant concentration was more than 10%. The overall results obtained in present study suggest that the vis-NIR spectroscopy along with suitable chemometric techniques have a great potential for rapid measurement of adulteration level in DCP.

Spatially-resolved spectroscopy (SRS) enables better interrogation of tissue properties at different depths, and it thus has the potential for enhancing quality assessment of horticultural products like tomato, which are heterogeneous in structure and chemical composition. This research was aimed at assessing quality of tomato fruit by using a newly developed SRS system with 30 detection optic fibers covering the wavelength range of 550–1650 nm and comparing its performance with two conventional single-point (SP) spectroscopic instruments covering the visible and shortwave near-infrared (Vis/SWNIR) (400–1100 nm) and near-infrared (NIR) (900–1300 nm) regions, respectively. Spatially-resolved (SR) spectra and SP interactance spectra were acquired for 600 ‘Sun Bright’ tomato fruit. Partial least squares (PLS) models for individual SR spectra and their combinations and for SP Vis/SWNIR and NIR spectra were developed for prediction of soluble solids content (SSC) and pH. Results showed that SSC and pH predictions by SRS varied depending on the light source-detector distance, with the correlation coefficient of prediction (rp) ranging 0.608–0.791 and 0.688–0.800, respectively. Combinations of two or more SR spectra resulted in better, more consistent SSC and pH predictions. SR predictions of pH (rp = 0.819) were better than for SP Vis/SWNIR (rp = 0.743) and NIR (rp = 0.741) predictions, whereas SR predictions of SSC (rp = 0.800) were comparable to SP NIR predictions (rp = 0.810) but better than SP Vis/SWNIR predictions (rp = 0.729). This research showed that owning to its ability of acquiring spatially-resolved spectral information, the SRS technique has advantages over conventional SP spectroscopy for enhancing quality assessment of tomatoes.

The presence of sunn pest-damaged grains in wheat mass reduces the quality of flour and bread produced from it. Therefore, it is essential to assess the quality of the samples in collecting and storage centers of wheat and flour mills. In this research, the capability of visible/near-infrared (Vis/NIR) spectroscopy combined with pattern recognition methods was investigated for discrimination of wheat samples with different percentages of sunn pest-damaged. To this end, various samples belonging to five classes (healthy and 5%, 10%, 15% and 20% unhealthy) were analyzed using Vis/NIR spectroscopy (wavelength range of 350–1000 nm) based on both supervised and unsupervised pattern recognition methods. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the unsupervised techniques and soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) as supervised methods were used. The results showed that Vis/NIR spectra of healthy samples were correctly clustered using both PCA and HCA. Due to the high overlapping between the four unhealthy classes (5%, 10%, 15% and 20%), it was not possible to discriminate all the unhealthy samples in individual classes. However, when considering only the two main categories of healthy and unhealthy, an acceptable degree of separation between the classes can be obtained after classification with supervised pattern recognition methods of SIMCA and PLS-DA. SIMCA based on PCA modeling correctly classified samples in two classes of healthy and unhealthy with classification accuracy of 100%. Moreover, the power of the wavelengths of 839 nm, 918 nm and 995 nm were more than other wavelengths to discriminate two classes of healthy and unhealthy. It was also concluded that PLS-DA provides excellent classification results of healthy and unhealthy samples (R2 = 0.973 and RMSECV = 0.057). Therefore, Vis/NIR spectroscopy based on pattern recognition techniques can be useful for rapid distinguishing the healthy wheat samples from those damaged by sunn pest in the maintenance and processing centers.

Sun injury (sunburn) and sunscald (delayed sunburn) postharvest have been well characterized on apples but not on pears. In fact, a sunscald-type disorder has only been noticed, commercially, in the last five years on pears grown under increasingly warmer conditions. ‘Packham’s Triumph’ pears with different sunburn severity (healthy, mild, moderate, and severe) were harvested from three commercial orchards in Chile and stored in air (0 °C, >80 % RH) for six months. Sunscald incidence, skin color (colorimeter L, a*, b*) and peel diffuse reflectance (400−1100 nm) were recorded monthly. Sunscald symptoms (i.e., skin browning) started to appear 2–3 months into cold storage and only on fruit with sunburn. Among the color coordinates, L decreased as storage time progressed while a* increased and was the most effective parameter to separate sunburn categories. Interval partial least squares regression (iPLS) found the spectral reflectance from the wavelength regions 500−600 nm, 650−700 nm, and 800−850 nm separated the sunburn categories during storage. Conversely, fruit that developed postharvest sunscald had significantly lower L and b* values (P ≤ 0.05), and lower overall reflectance intensities at harvest, regardless of sunburn severity. The best sunscald model was achieved using the 550–600 nm band, with a sensitivity of 77 % and specificity of 75 %.

Golden pompano (Trachinotus ovatus) quality forecasting method utilising Vis/NIR spectroscopy combined with electronic nose (EN) was investigated in this article. Responses of Vis/NIR spectroscopy and EN to pompanos stored at 4°C were measured for 6 days. Physical/chemical indexes including texture, total volatile basic nitrogen, pH, total viable counts, and human sensory evaluation were synchronously examined as quality references. Chemometric methods including principal component analysis (PCA) and stochastic resonance (SR) were employed for spectroscopic and EN data analysis. Physicochemical examination demonstrated that fish quality decreased rapidly during storage. PCA qualitatively classified freshness degree of pompano samples, while SR signal-to-noise ratio (SNR) spectrum using SNR maximum quantitatively characterised quality for all samples. Golden pompano quality predictive models were developed based on spectroscopy, EN, and spectroscopy combined with EN, respectively. Results demonstrated that the model developed based on spectroscopy combined with EN presented a forecasting accuracy of 93.3%.

The objectives of this study were to (i) evaluate the ability of Vis-NIR spectroscopy to predict soil organic carbon (SOC) and CaCO3 content in the heterogeneous agricultural soils from Dalmatia, Croatia and (ii) to compare the performance of two multivariate calibration techniques: partial least square regression (PLSR) and support vector machine regression (SVMR). The reflectance spectra of a total of 250 top-soils (0-25 cm) samples were collected in the laboratory using a portable Terra Spec 4 Hi-Res Mineral Spectrometer with a wavelength range 350-2500 nm. The coefficient of determination (R2), the residual prediction deviation (RPD) and the root mean square error (RMSE) were used for the model evaluation. The CaCO3 prediction derived by PLSR and SVMR with R2 (0.86 and 0.88) and RPD (2.67 and 2.82), respectively are considered good prediction models. The SOC prediction with SVMR (R2 0.84 and RPD 2.43) indicates good prediction and approximate quantitative prediction with PLSR with R2 of 0.78 and RPD of 1.94. Our results showed that (i) CaCO3 and SOC estimations were obtained with acceptable accuracy using Vis-NIR spectroscopy, (ii) the SVMR method produced more accurate estimations of selected soil properties compared to LSR, and (iii) Vis-NIR spectroscopy, in combination with SWMR can be recommended as a rapid and inexpensive method for screening of the CaCO3 and SOC content., Ciljevi ove studije bili su: (i) procijeniti mogućnost Vis-NIR spektroskopije za procjenu sadržaja organskog ugljika tla (OC) i CaCO3 u heterogenim poljoprivrednim tlima iz Dalmacije, Hrvatska, i (ii) usporediti učinkovitost dviju multivarijantnih kalibracijskih tehnika: partial least square regression (PLSR) i support vector machine regression (SVMR). Spektralni otisci ukupno 250 uzoraka tla (0-25 cm) uzeti su u laboratoriju korištenjem prijenosnoga Terra Spec 4 hi-res spektrometra, s rasponom valne dužine 350-2500 nm. Koeficijent determinacije (R2), rezidualno predikcijsko odstupanje (RPD) i korijen srednje kvadratne greške (RMSE) korišteni su za procjenu modela. Predviđanja CaCO3 korištenjem PLSR-a uz R2 od 0,86 i RPD 2,67 te SVMR-a uz R2 od 0,88 i RPD 2,82 indiciraju dobre prognozne modele. Predviđanje OC-a korištenjem SVMR-a (R2=0,84 i RPD=2,43) ukazuje na dobru procjenu, a PLSR metoda (R2=0,78 i RPD=1,94) na približnu kvantitativnu procjenu. Naši su rezultati pokazali (i) prihvatljivu točnost procjene CaCO3 i OC-a korištenjem Vis-NIR spektroskopije, (ii) da je SVMR metoda dala točnije procjene odabranih svojstava tla od PLSR-a i (iii) da se Vis-NIR spektroskopija, u kombinaciji sa SWMR-om može koristiti kao brza i jeftina metoda za određivanje CaCO3 i sadržaja organskog ugljika u tlu.