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In this research, an optical system based on fibre optic Vis/NIR spectroscopy combined with chemometrics methods and software as a graphical user interface (GUI) was developed and presented for fast and non-destructive detection and determination of pesticide residues in agricultural products (a case study on diazinon in intact cucumbers). Vis/NIR spectra of cucumber samples without and with different concentrations of diazinon residue were analyzed at the range of 450–1000 nm. Partial least squares (PLS) regression models were developed based on chemical reference measurements and the spectral information of the samples after performing different pre-processing methods. Moreover, partial least squares-discriminant analysis (PLS-DA) models were developed based on different spectral pre-processing techniques to classify cucumbers with contents of diazinon below and above the maximum residue limits (MRL) as safe and unsafe samples, respectively. Finally, user-friendly software as a GUI was created based on the best PLS and PLS-DA models developed for prediction of diazinon contents in the samples and for classification of intact cucumbers by the absence/presence of diazinon residues, respectively. Evaluation of the system and software designed based on the best developed PLS and PLS-DA models indicated good performance for measuring and detection of diazinon residue in cucumbers. It was concluded that the designed system and software based on Vis/NIR spectroscopy combined with chemometrics methods can be utilized for fast and non-destructive safety control of intact cucumbers by the absence/presence of diazinon residues. It can also be generalized for detection of other pesticide residues in agricultural products if developing their appropriate models is feasible. [ABSTRACT FROM AUTHOR]

The feasibility of utilizing near-infrared (NIR) spectroscopy has been recently assessed for rapid and non-destructive detection of internal insect infestation in some fruits. Based on the findings, this technology can be used for on/in-line inspection of the fruits in terms of insect infestation if suitable instrument is selected for accurate spectral measurements and system development. The spectral range and optical measurement mode are two of the most important factors which can affect the accuracy of the spectral measurements and detection models. The aim of this study is meta-analysis of these factors' effects on the ability of NIR-based spectroscopy for non-destructive detection of hidden insect infestation in fruits. Eight studies (65 observations) were extracted based on the criteria of this study. Overall, utilizing NIR-based spectroscopy led to 13.98% error (95% CI = 10.69–17.27%) for non-destructive detection of hidden insect infestation in fruits. Spectral ranges of Vis/SWNIR (above 300 up to 1100 nm), NIR (above 780 up to 2500 nm), and Vis/NIR (above 300 up to 2500 nm) showed errors of 21.71% (95% CI = 16.56–26.86%), 13.30% (95% CI = 5.24–21.36%), and 13.65% (95% CI = 5.9–21.4%), respectively. It was noted that wavelengths above 1100 nm (NIR region) are more useful to detect insect infestation in fruit. Results also indicated that optical measurement modes of interactance, reflectance, and transmittance had errors of 6.66% (95% CI = 4.18–9.14%), 15.73% (95% CI = 10.99–20.47%), and 16.04% (95% CI = 7.26–24.82%), respectively. This meta-analysis suggests that utilizing interactance mode for spectra measurement in NIR-based spectroscopy can increase the accuracy of discrimination of insect infested fruits especially when the spectral range of the spectrometer is Vis/SWNIR. Moreover, it should be selected a spectrometer with the wavelength range of NIR or Vis/NIR when using reflectance or transmittance mode is necessary for developing an in/on-line system to detect insect infestation in fruits. [ABSTRACT FROM AUTHOR]

Highlights • Wavelet transform was used as pre-processing tool in Vis/NIR spectral analysis. • Developed models showed better regression performance than previous studies. • Wavelet transform showed acceptable performance for denoising of spectral data. Abstract Quality assessment of fruits and vegetables is important for all involved in production, processing, trading and even consumption of the products. This study was conducted for non-destructive estimation of moisture content (MC), soluble solids content (SSC), pH and firmness of Gala apple samples using near-infrared spectroscopy in the 350–2500 nm range by application of wavelet transform for pre-processing of raw spectral data. Wavelet transform was combined with other usual pre-processing functions widely used for constructing PLS regression models. The models' predictive capability was evaluated using correlation coefficient of determination (R2), root mean square error of estimation (RMSE) and correlation coefficient (R). The best calibration and validation results were obtained for MC with 8 factors, R2 C = 0.90, RMSEC = 0.0042, R2 CV = 0.88, and RMSECV = 0.0047. The PLS regression parameters for SSC with 9 model factors, were R2 C = 0.90, RMSEC = 0.37, R2 CV = 0.86, and RMSECV = 0.45. The calibration and validation model parameters for pH and firmness with 10 model factors were R2 C = 0.87, RMSEC = 0.04, R2 CV = 0.84, RMSECV = 0.05, and R2 C = 0.76, RMSEC = 2.23, R2 CV = 0.0.59, and RMSECV = 3.04, respectively. In the prediction set, the model that was developed for moisture content had RMSEP = 0.009, R2p = 0.6. The R2p, and RMSEP for SSC were, 0.87 and 0.55, respectively. The model prediction parameters for pH were 0.72 and 0.06 for, R2p and RMSEP, respectively. Finally, predicted firmness of apple samples were acceptable (R2p = 0.65 and RMSEP = 3.86). Because of noise removal ability, application of wavelet transform for pre-processing on spectral data led to accurate, simple and fast development of PLS regression models. [ABSTRACT FROM AUTHOR]

The quality of agricultural products is of high importance in terms of consumer interest, determining market acceptance, and thus, directly affects storage and post-harvest processing operations. Quality measurement of fruits, vegetables and food products is at the center of attention by the food industry. Non-destructive measurements of quality parameters have been conducted on many agricultural products and have proved to be rapid and accurate in estimating the quality factors involved. While, non-destructive methods are very useful for quality testing, the recent interest by consumers, researchers, and the food industry in the application of new portable and/or handheld non-destructive devices for quality assessment of agricultural products has added a new dimension to the issue. Because these devices are small-sized, low-cost, low-weight, and easy to use, they can be utilized by farmers, quality inspectors, and even consumers. Thus, researchers have focused on developing portable non-destructive devices for a variety of food items. This review, examines the latest reports on the design and development of dedicated non-destructive portable and/or handheld devices for quality monitoring of agricultural products. [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]

• Safety assessment of lettuce based on E. coli using the Vis/NIR spectra is possible. • SIMCA & SVM can be more reliable than PCA & HCA to distinguish unsafe samples. • Microbial population amount cause a complex process in lettuce. • Chemical component of lettuces with E. coli concentration have a non-linear relationship. • PLSDA provides a robust qualitative model accurately classified lettuce samples. Escherichia coli is a main cause of microbial contamination in lettuce. Detection of microbial contamination is the essential key to ensure the consumption of fresh lettuce. In this investigation, the potential of Vis/NIR spectroscopy system with chemometrics analysis was probed for detecting different microbial loads (0.1, 0.2 and 0.3 ml concentration of E. coli solution) on the lettuce samples in the wavelength range of 350–1100 nm. Five different chemometrics analyses consist of soft independent modeling of class analogies (SIMCA), support vector machine (SVM), Partial least Squares Discriminant Analysis (PLS-DA) (supervised techniques), principal component analysis (PCA) and hierarchical cluster analysis (HCA) (unsupervised techniques) were used. The results proved that HCA were correctly clustered unsafe samples with 0.2 ml and 0.3 ml microbial contamination. However, some overlapping was observed between safe and unsafe samples with 0.1 ml microbial contamination. Vis/NIR spectral data with pattern recognition methods (SIMCA and SVM) can be obtained acceptable degree of classification (87.1% and 89.39% accuracy, respectively) between safe and unsafe samples. Compare with 5 different methods, the best model was recommended by PLS-DA using standard normal variation (SNV) + second derivate (D 2) pre-processing methods in 6 optimal selected wavelengths (520, 670, 700, 750, 900, 970 nm) with the minimum standard deviation of cross-validation (SECV = 0.176). Besides, a good correlation (r c = 0.989) between Vis/NIR spectral data and E. coli contamination proved the possibility of using Vis/NIR spectroscopy system to detection and evaluation of microbial contamination in lettuce. [ABSTRACT FROM AUTHOR]

Near-infrared transmittance spectroscopy combined with multivariate chemometric was investigated in the present work to predict the Polarization (Pol), soluble solids content (°brix), sucrose concentration and pH value of raw, thin and thick sugar beet juices (SBJs). All samples were scanned to obtained spectral information in the wavelength range of 860–1800 nm. The impacts of various pre-processing and spectra treatment methods, such as standard normal variate transformation (SNV), Moving Average (MA) and area normalization (AN) were also investigated. Partial least squares (PLS) regression was introduced to develop the calibration model relating quality parameter values to spectral data. Accordingly, the results show that the PLS models were acceptable for predicting the pol (R2 = 0.969), °brix (R2 = 0.984), sucrose (R2 = 0.921) and slightly pH value (R2 = 0.671) in SBJ samples. Results suggest that the near-infrared spectroscopy coupled with the PLS regression method can become a useable and fast tool for evaluation of quality parameters in SBJs. • Quality parameters of sugar beet juices have been measured using NIR spectroscopy. • The PLS regression predicts pol, brix and sucrose with high accuracy. • SNV, AN and MA pre-processing methods have been tested to improve the models. • NIR spectroscopy can be proposed as a rapid technique for beet juices analysis. [ABSTRACT FROM AUTHOR]