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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]

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]

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]

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]