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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]

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]

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]