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Conduct disorder is one of the most serious and complex complications in school-aged children. This study was conducted with the aim of comparing the effectiveness of Good Enough Parenting training with schema therapy approach and positive parenting program training on the parent-child conflict in mothers of children with conduct disorders. The method of the current research was a quasi-experimental one with pretest-post-test method plus a control group and a follow-up stage. The population included all mothers of students with conduct problems in the second grade of elementary school in Tehran in the academic year of 2020-2021. Out of this population, after the implementation of the Sprafkin test (the fourth edition) a sample of 60 participants (three groups of 20 people from mothers of children, including two experimental groups and one control group) were selected using the available sampling. They were given Sprafkin children's behavioral syndrome questionnaire and the parentchild conflict questionnaire. The results were analyzed using repeated measurements analysis of variance. The results indicated that both interventions led to a significant reduction in parent-child conflicts. Also, there was no significant difference between their effectiveness. In general, the results revealed that Good Enough Parenting training with schema therapy approach and positive parenting program training has a significant effect on parent-child conflict. The results of this study can be benefited in psychology, especially in the field of parenting. [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]

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 present study investigated the feasibility of visible near-infrared (Vis-NIR) spectroscopy method as a low-cost, quick and reliable method for the prediction of qualitative characteristics (fruit juice percentage, soluble solids, acidity, taste index and vitamin C) of an export variety of Persian pomegranate. For this purpose, pomegranate samples were first analyzed by Vis-NIR spectroscopy, followed by destructive reference methods for quality determination. Subsequently, the data from Vis-NIR were evaluated with different pre-processing methods, among which the combination of the Savitzky–Golay (SG) smoothing model and standard normal variation (SNV) normalization model, generates the best outcomes in predicting six desired qualitative characteristics by partial least square regression (PLSR) model. Fruit juice percentage and pH presented the best prediction models with a correlation coefficient (r P) of 0.98 for both, and root mean square errors of prediction (RMSEP) equal to 0.036 and 0.041, respectively. The values of r p for all desired parameters in this study were higher than 0.95, which pointed to the excellent accuracy of the PLSR models in the prediction of pomegranate qualitative features. It can be concluded from the present study that, Vis-NIR spectroscopy using the PLSR model as a non-destructive functional approach is capable of verifying the quality of Persian export variety pomegranate as an online technique with the aim of grading products before the export phase in order to augment the product marketability. [Display omitted] • Twelve quality parameters of fruit determined through destructive tests. • The most important quality parameters were selected for non-destructive prediction. • Vis-NIR was able to predict selected quality parameters with excellent accuracy. • The fruit juice percentage was predicted for the first time as a quality parameter. • The proposed method can be utilized in the grading stage prior to the export phase. [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]

In this study, a portable optical instrument was designed and developed for real-time nondestructive determination of fruit ripeness. A prototype of the instrument using inexpensive parts was developed and calibrated for apple ripeness detection based on moisture content, soluble solids content, pH, and firmness. An array of six optical point sources (light-emitting diodes (LEDs) that emit light at 520-, 670-, 920-, 970-, 1050-, and 1320-nm wavelengths) were selected using visible/near infrarred (Vis/NIR) spectral analysis on apple samples. The instrument was equipped with optical fibers, optical sensors, and an Arduino board for sending and receiving the waves and processing the received signals, as well as controlling the operation of the instrument. The decision tree method was utilized to create a classification model for training the instrument to classify ripe and unripe apples. Test results showed acceptable performance of the developed instrument for apple ripeness estimation and proved useful in classifying fruits into ripe and unripe groups based on their internal quality. [ABSTRACT FROM AUTHOR]

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]

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 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]

• 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]

Initial low cost of pesticides and lack of knowledge on the part of agricultural producers has led to an overuse of pesticides, with dangerous consequences. On the other hand, today, pesticides have an important role in the increment of agriculture products. Therefore, in recent years, the major aims of agricultural, environmental and even the food industry researchers have been to reduce the usage of chemical materials in agricultural applications. To achieve this, variable rate application in sprayers using different technologies such as machine vision, ultrasonic sensors, spectrometry, laser scanning, as well as pest identification and classification, have been applied in recent studies. This paper reviews these works reported in the 21 century. In addition, the best result is suggested in the conclusion section for future studies. [ABSTRACT FROM AUTHOR]

Absorption and scattering properties of product change as moisture content is reduced, but it has not been investigated how these changes are correlated. This study was aimed to measure and test the feasibility of using optical properties in predicting the moisture content of sliced apple samples during hot air drying. In this investigation, the noninvasive backscattering laser imaging technique at three wavelengths (650, 780, and 880 nm) and Farrell’s diffusion theory were used to determine absorption and reduced scattering coefficients. Artificial neural network model was applied to correlate the optical coefficients and moisture content of samples. The highest correlation between above-mentioned parameters was found at 780 nm. The best moisture content prediction result was obtained when absorption and reduced scattering coefficients were combined at three wavelengths withRp = 0.984. The results suggested that this method can be effectively used to predict the moisture content and control the drying process. [ABSTRACT FROM PUBLISHER]

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]