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Abstract In this research, the feasibility of utilizing visible/near-infrared (Vis/NIR) spectroscopy as an optical non-destructive technique combined with both supervised and unsupervised pattern recognition methods was assessed for detection of Ectomyelois ceratoniae , carob moth, infestation in pomegranates during hidden activity of the larvae. To this end, some fruits were artificially contaminated to the carob moth larvae. Vis/NIR spectra of the blank samples and the contaminated pomegranates without and with external visual symptoms of larvae infestation were analyzed one and two weeks after contaminating the samples as three groups of "Healthy", "Unhealthy-A" and "Unhealthy-B", respectively. Principal component analysis (PCA) as unsupervised pattern recognition method was used to verify the possibility of clustering of the pomegranate samples into the three groups. Discriminant analysis (DA) based on PCA was also used as a powerful supervised pattern recognition method to classify the samples. The calibration models of linear, quadratic and Mahalanobis discriminant analyses were developed based on different spectral pre-processing techniques. The best PCA-DA model was obtained using Mahalanobis distance method and first derivative (D1) pre-processing. The total percentage of correctly classified samples with the best calibration model was 97.9%. The developed model could also predict unknown samples with total percentage of correctly classified samples of 90.6%. It was concluded that Vis/NIR spectroscopy combined with pattern recognition method of PCA-DA can be an appropriate and rapid technology for non-destructively screening the pomegranates for detection of carob moth infestation during hidden activity of the larvae. Graphical Abstract Unlabelled Image Highlights • Rapid detection of carob moth infestation during hidden activity of larvae in pomegranate is possible. • Vis/NIR spectroscopy can be used for health screening the pomegranates, non-destructively. • PCA-DA provides excellent classification results of healthy and unhealthy samples. [ABSTRACT FROM AUTHOR]

In this research, a non-destructive and low-cost approach based on Vis/NIR spectroscopy was developed for rapid measurement of nitrate concentrations in cucumbers and screening of the samples based on maximum limit of nitrate accumulation. Vis/NIR spectra of intact cucumbers including both field and greenhouse samples with different concentrations of nitrate were analyzed. PLS models were developed based on the pre-processed spectra and reference measurements. Moreover, PCA-DA classification models with different methods of linear, quadratic and Mahalanobis were developed to discriminate the samples based on maximum limit of nitrate accumulation according to Iranian National Standardization Organization (INSO) and World Health Organization (WHO), as well as based on the type. The best PLS model for prediction of nitrate concentrations in the cucumbers was achieved using D 2 pre-processing method (r p =0.87, SEP=60.92 mg kg−1, RPD=2.07). It was concluded that Vis/NIR spectroscopy is adequate for screening the cucumbers based on their nitrate concentrations. The results also confirmed that this technology combined with PCA-DA is appropriate (accuracy=72.22%, sensitivity=76.74%, specificity=54.55%) and fairly suitable (accuracy=64.81%, sensitivity=59.26%, specificity=70.37%) for screening of the cucumbers based on maximum limit of nitrate accumulation according to INSO and WHO, respectively. It was excellent technology for classification of the cucumbers based on their type (accuracy=94.44%, sensitivity=93.55%, specificity=95.65%). Consequently, the proposed spectroscopic approach can be used for safety assessment of cucumbers as a rapid method with easy and out-of-laboratory usability. • Non-destructive measurement of nitrate concentrations in cucumbers is possible using Vis/NIRS. • Non-destructive and rapid screening of cucumbers based on maximum limit of nitrate is possible. • Vis/NIRS can be used for developing a portable system for safety assessment of cucumbers. [ABSTRACT FROM AUTHOR]

• A numerical example is provided for using non-destructive spectroscopy of safety control. • Monitoring system built on a spectral analysis of different microbial population assessment. • The suggested method of using GA algorithm is proposed for adaptive sensor development. Microbial contamination of vegetables is always known as a major food safety challenge. Persian leek is one of the most widely used herbs. Detection of Escherichia coli (E. coli ATCC 8739) in vegetables is very important issue for post-harvest management. Non-destructive assessment of E. coli ATCC 8739 contamination of leek as a main aim of this study were investigated using visibe/near-infrared spectroscopy combined with three variable selection approaches and partial least squares discriminant analysis as a promising chemometrics tool: Genetic Algorithm-PLSDA, interval PLS algorithm-DA and variable influence on projection scores-PLSDA. Reflectance spectra in interactance mode were measured on different treatments of E. coli ATCC 8739 solution (0.1, 0.2 and 0.3 mL) on leek samples in the spectral range of 350–1100 nm. The spectra (R) were captured at three different positions of sample by random in triplicate. The calibration and internal validation sets were created with 150 (75 %) and 50 (25 %) leek samples, respectively. In order to prevent incorrect results due to over-fitting, the final developed model has been deeply validated with external validation set of 50 samples. Five key features selected by genetic algorithm showed high performance of E. coli ATCC 8739 detection with 100 %, 98 % and 0.8 of sensitivity, specificity and classification error, respectively. Besides, a high prediction ability (>95 % correct classification in internal validation set and >89 % in external validation set) were achieved between 490, 500, 570, 740, and 970 nm spectral distances and the presence of E. coli ATCC 8739 contamination. In general, The Vis/NIR spectroscopy method for non-destructive detection of E. coli ATCC 8739 contamination on Persian leek in food safety application is feasible and practical. We further demonstrate the capability of genetic algorithm to detect the bacterial cells in the samples at the early growth stage. [ABSTRACT FROM AUTHOR]