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The developed multispectral imaging system was tested in the current study for its detection accuracy of aflatoxin B1 (AFB1) in single kernel almonds. Based on our previous study, characteristic wavelengths for aflatoxin B1 (AFB1) were selected from the hyperspectral imaging data and a prototype of almond sorter was designed and developed using multispectral imaging system. Five levels of AFB1 concentrations, viz. 0.25, 0.50, 0.75, 1.00 μg/kernel were used for testing of the sorter. Artificial neural network (ANN) was used to predict the AFB1 contamination levels and four discriminant models, viz. support vector machine (SVM), logistic regression (LR), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were analyzed to classify the contaminated kernels from the normal ones. Results indicated that ANN was very efficient in prediction of AFB1 threshold contamination levels of 0.250 µg/g with accuracy of 99.300%, 98.100%, 95.700%, 86.200% and least error rate of 0.042, 0.077, 0.109 µg/g, 0.187 µg/g respectively for training, cross-validation, prediction and testing using external test set data. The two-class LR model accurately discriminated the contaminated from the noncontaminated almonds with a threshold of 0.25 µg/g and the correct classification rate of 90.800% and 95% for cross-validation and external test datasets, respectively. The multispectral imaging system demonstrated its potential application to rapid online detection of AFB1 contamination levels in single almond kernels and discrimination of contaminated kernels at the same time. • A multispectral imaging system was tested for its accuracy in detection of AFB1 in single kernel almonds. • ANN used for prediction of AFB1 in the almond kernels with R v of 0.98 & RMSEV of 0.077 µg/g. • SVM, LR, LDA, QDA models were developed to classify the control and contaminated kernels. • LR model accurately discriminated the contaminated kernels from the control with CCR of 90.8%. [ABSTRACT FROM AUTHOR]

The current work focused on rapid and non-destructive determination of moisture content (MC), free fatty acids (FFA) and peroxide value (PV) in shelled almonds, using reflectance NIR-hyperspectral imaging (HSI). Sample set of 354 and 235 almond kernels were treated to vary the MC, rancidity (FFA and PV), respectively and used for model development. The sample set for each response was divided into calibration and testing set in the ratio of 70:30. Reference MC, FFA and PV were measured using wet analysis methods and their association with the spectral data were modelled using partial least squares regression. Superior models were obtained from the full-spectrum (900–1700 nm) data with R p 2 values of 0.957, 0.970, 0.955 and RMSE P values of 0.41%, 0.108%, 0.453 mEq for MC, FFA and PV, respectively. Competitive adaptive reweighted sampling method was used to select the feature wavelengths for rapid quantification. Multiple linear regression models were developed using the feature wavelengths having good predictability (R p 2 values: 0.941, 0.903, 0.886 and RMSE P values: 0.494%, 0.162%, 0.658 mEq for MC, FFA and PV, respectively). The current findings demonstrated great feasibility in industrial deployment of HSI technique for non-destructive estimation of moisture and rancidity indices in almonds and other nuts. [Display omitted] • Hyperspectral imaging was used to develop non-destructive method for determination of MC, FFA and PV of almond. • Full spectrum PLSR models predicted MC, FFA and PV with R p 2 values of 0.957, 0.970, 0.955, respectively. • CARS-PLS was used to select the feature wavelengths for rapid and accurate quantification. • MLR models with feature wavelengths showed R p 2 values of 0.941, 0.903, 0.886 for MC, FFA and PV, respectively. [ABSTRACT FROM AUTHOR]

Almonds are highly susceptible to Aflatoxin B1 (AFB1) contamination, which can result in significant economic losses. Current detection techniques are destructive, time consuming and unfit for in-line application. This study investigated the potential of hyperspectral imaging in the near infrared (NIR) range (900–1700 nm) to develop a rapid and non-destructive protocol for determination of AFB1 content of single almond kernels. Almond kernels were treated to varying AFB1 concentration by artificial infection using standard AFB1 solution and used for Experimentation. Reference AFB1 concentration and their association with the spectral data were modelled using partial least squares regression (PLSR) combined with suitable spectral preprocessing techniques. Superior models were obtained with full-spectrum regression models with R2 and RMSEP values of 0.958 and 0.089 μg/g, respectively. Competitive-adaptive reweighted sampling (CARS) was used to select the feature wavelengths for rapid quantification of AFB1 at commercial scale. Multiple linear regression (MLR) models were developed using the selected wavelengths for its applicability in multispectral imaging systems. Results showed that the MLR model achieved good prediction capabilities with R2 and RMSEP values of 0.948 and 0.090 μg/g, respectively. The results demonstrated that hyperspectral imaging had potential for rapid and nondestructive determination of AFB1 concentration in single kernel almonds. • Hyperspectral imaging was used to develop non-destructive method for detection of aflatoxin B1 in almonds. • Calibration models were developed using PLSR and suitable preprocessing techniques. • CARS-PLS was used to select the feature wavelengths for rapid and accurate quantification. • MLR models with feature wavelengths showed good prediction capabilities. [ABSTRACT FROM AUTHOR]