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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 (R2C and R2CV), and Residual Predictive Deviation (RPD) were used. The best prediction belonged to reducing sugar with statistical values of R2C = 0.99, R2CV = 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 R2C = 0.93, R2CV = 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. [ABSTRACT FROM AUTHOR]

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]

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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 (R 2  = 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. [ABSTRACT FROM AUTHOR]

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]

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]

The feasibility of using visible/near-infrared (Vis/NIR) spectroscopy was assessed for non-destructive detection of diazinon residues in intact cucumbers. Vis/NIR spectra of diazinon solution and cucumber samples without and with different concentrations of diazinon residue were analysed at the range of 450–1000 nm. 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 MRL as safe and unsafe samples, respectively. The best model was obtained using a first-derivative method with the lowest standard error of cross-validation (SECV = 0.366). Moreover, total percentages of correctly classified samples in calibration and prediction sets were 97.5% and 92.31%, respectively. It was concluded that Vis/NIR spectroscopy could be an appropriate, fast and non-destructive technology for safety control of intact cucumbers by the absence/presence of diazinon residues. [ABSTRACT FROM AUTHOR]

Reflectance Visible/near-infrared spectroscopy was used to analyze the effect of peel on the non-destructive soluble solids content prediction of oranges. The spectra were interpreted and partial least squares model was developed for both intact and peeled oranges at two wavelength ranges of 450–1000 and 700–1000 nm. Results showed that the wavelengths of the main absorbance peaks on the spectra of the intact and peeled samples were the same (p> 0.05). Moreover, the developed models yielded a good prediction accuracy of soluble solids content (2.5 < standard deviation ratio < 3) for intact oranges as well as the peeled fruits at the spectral ranges of 450–1000 and 700–1000 nm (p> 0.05). It was also noted that the effect of peel chemical composition can be ignored in non-destructive spectroscopy of intact citrus for determination of soluble solids content. [ABSTRACT FROM PUBLISHER]

Abstract: The feasibility of reflectance Vis/NIR spectroscopy was investigated for taste characterization of Valencia oranges based on taste attributes including soluble solids content (SSC) and titratable acidity (TA), as well as taste indices including SSC to TA ratio (SSC/TA) and BrimA. The robustness of multivariate analysis in terms of prediction was also assessed. Several combinations of various preprocessing techniques with moving average and Savitzky–Golay smoothing filters, standard normal variate (SNV) and multiplicative scatter correction (MSC) were used before calibration and the models were developed based on both partial least squares (PLS) and principle component regression (PCR) methods. The best models obtained with PLS method had root mean square errors of prediction (RMSEP) of 0.33°Brix, 0.07%, 1.03 and 0.37, and prediction correlation coefficients (r p) of 0.96, 0.86, 0.87 and 0.92 for SSC, TA, SSC/TA, and BrimA, respectively. It was concluded that Vis/NIR spectroscopy combined with chemometrics could be an accurate and fast method for nondestructive prediction of taste attributes and indices of Valencia oranges. Moreover, the application of this technique was suggested for taste characterization, directly based on BrimA which is the best index related to fruit flavor rather than determination of SSC or TA alone. [Copyright &y& Elsevier]

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]

• A new approach for in-situ monitoring of apple nutrient contents was proposed. • Apple leaf spectra can replace costly and destructive analysis to predict NPK. • The RF model with Rfrog wavelength selection achieved the best results. • PLSR-based models are not recommended for nutrient estimation. • The approach can be used to develop a portable instrument to estimate NPK concentrations. Timely and rapid monitoring of apple trees nutrition status is vital for accurate management of nutrient fertilizers in order to improve the yield and quality, as well as to reduce the risk of environmental degradation. As a most frequently used method, tissue analysis used to assess the nutritional status of apple tree leaves is laborious, costly, time-consuming, environmentally unfriendly, and destructive. Ground-based sensors are able to efficiently provide information on nutritional status using leaf spectra reflectance. This research aims to establish a novel cost-effective and non-destructive approach for rapidly estimating the status of nitrogen (N), phosphorus (P), and potassium (K) in apple tree leaves based on Visible/Near-infrared (Vis/NIR) spectroscopy (500–1000 nm) coupled with machine learning. The Vis/NIR spectra of apple trees' leaves were acquired. Then, leaf chemical contents of NPK elements were considered as reference points. Different pre-processing techniques were used to pre-treat the spectra. Four different chemometrics analysis consist of support vector machine (SVM), Artificial neural network (ANN), Random Forest (RF) and partial least square (PLS) were applied to predict NPK contents in comparison to actual values. In order to simplify the models, the sensitive wavelengths were extracted using three wavelength selection approaches, variable importance in projection (VIP), partial least squares (PLS), and random frog (Rfrog). The extracted feature wavelengths from PLS, VIP, and Rfrog methods were widely distributed in the visible and near-infrared regions of the spectrum. The performance of the developed models was tested using Residual Prediction Deviation (RPD) and the Ratio of Performance to Interquartile Distance (RPIQ). The results demonstrated that among all models, the non-linear modeling methods were superior to the linear model. The best results for estimation of Nitrogen (N), Phosphorus (P), and Potassium (K) elements were achieved by the models of MSC + D 2 -Rfrog-RF (r p = 0.985, RMSEP = 0.029%, RPD = 8.77, RPIQ = 7.72), SNV + D 2 -Rfrog-RF (r p = 0.977, RMSEP = 0.0053%, RPD = 6.42, RPIQ = 5.09) and SNV + D 2 -Rfrog-RF (r p = 0.978, RMSEP = 0.018%, RPD = 8.16, RPIQ = 7.01), respectively. The findings of the current approach may provide an efficient approach to predict in-situ NPK contents of apple trees based on leaf spectral reflectance. [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]