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Biodiesel (fatty acid methyl or ethyl esters) is made from vegetable oil or animal fat (triglycerides) reacting with methanol or ethanol using a catalyst (lye). It is safe, biodegradable, and produces less air pollutants than petroleum-based diesel or recycled restaurant greases. With the increasing demand of green energy source and the decreasing of fossil fuel, biodiesel has gained increasing attention as one of the alternative fuels. 100% biodiesel (B100) was used in this study. Experimental samples with water content of 0, 2.50%, 5.00%, 7.50% and 10.0% were set. There were 35 samples for every treatment with different water contents, and total 175 samples. 116 samples were selected for calibration set, and 58 samples for prediction set based with Kennard-Stone (K-S) method. Visible and near infrared spectra (Vis-NIR) technique which was a nondestructive and rapid method, was used to measure the water content in biodiesel. Samples were scanned using the ADS Handheld FieldSpec spectrometer and spectra of samples were acquired. Principal component analysis (PCA) was used to compress spectral data and observe the cluster's situation of biodiesel with different water contents. The scores plot showed a good cluster distribution and the total accumulated variance of PC-1 and PC-2 was up to 99.3%. Random Frog algorithm was applied to extract spectral feature. Then, 8 sensitive wavelengths (563, 560, 642, 565, 562, 493, 559 and 779 nm) were selected respectively. Spectral feature and different water contents were set as input values of partial least squares regression (PLSR) and least squares-support vector machine (LS-SVM) models. It was showed that LS-SVM and PLSR with full spectra had good results, while the variables were too much (116×591) compared with the regression models (116×8). Results of the Random Frog-LS-SVM were better than the Random Frog-PLSR. R of the non-linear LS-SVM models with spectral feature extracted by Random Frog was higher than 0.965, RMSEC of 0.722, RMSEP of 0.520. Sensitive wavelengths extracted were good for eliminating the interfering spectral and improving the accuracy of the model. Results indicated that the Random Frog-LS-SVM as a satisfactory model can measure the water content in biodiesel accurately, which could provide a reference for practical application. [ABSTRACT FROM AUTHOR]

Highlights: [•] Obtain the spectra and image feature of crack jujube based on a Vis/NIR hyperspectral imaging system. [•] Extract characteristic wavebands with chemometrics methods of PLSR, SPCA, SICA. [•] Achieve qualitative discrimination of crack feature using LS-SVM discrimination models with characteristic wavebands. [•] Identify crack feature of fresh jujube using a developed image processing algorithm. [Copyright &y& Elsevier]

• Providing a novel method to detect determine apple valsa canker (AVC) at different infection stages using molecular vibrational spectroscopy information.Selecting several variables based on x -loadings and CARS algorithm. • Developing LS-SVM models for diagnosing AVC infection stages and using ROC to evaluate models. Detecting of apple valsa canker (AVC), which is caused by Valsa mali Miyabe et Yamada, at early infection stage is beneficial to disease prevention and control for ensuring yield and quality of apples. Applying simple, economical and non-destructive rapid detection method for the early diagnosis of AVC is of great significance for precision management of orchards. In this research, near-infrared (NIR) spectroscopy (900−1700 nm) and Raman scattering (0−2000 cm−1) combined with machine learning algorithms were employed to diagnose 3 infection degrees of AVC (healthy, disease 1, disease 2) based on optimal variables which were selected by chemometric methods. NIR and Raman spectroscopy were obtained using portable spectrometers in reflection mode, respectively. Firstly, adaptive iterative reweighting partial least squares (air-PLS) was utilized to remove fluorescence background in Raman spectra. Secondly, clustering analysis was developed using principal component analysis (PCA). After that, optimal variables were selected by x -loadings (XLs) of PCA and competitive adaptive reweighted sampling (CARS) algorithm, respectively. Four optimal wavelengths at 983, 1156, 1395, and 1457 nm from NIR spectra and five optimal wavenumbers at 175, 229, 326, 409, and 1523 cm−1 from Raman spectra were identified by XLs. Six optimal wavelengths at 943, 949, 967, 1240, 1632, 1666 nm from NIR spectra and eight optimal wavelengths at 412, 1500, 1585, 1596, 1599, 1602, 1672 and 1709 cm−1 from Raman spectra were selected according to CARS. Finally, AVC diagnosing models were developed using least square support vector machine (LS-SVM), and receiver operating characteristic (ROC) curves were applied to evaluate the performance of the classification models. Overall, the relatively good classification results on optimal variables (94.67% and 97.33% in NIR, 89.33% and 89.33% in Raman) were obtained using LS-SVM. This study confirmed that molecular vibrational spectroscopy techniques (NIR and Raman) is promising for early detecting AVC and providing a practical way for diagnosing diseases in large-scale orchards. [ABSTRACT FROM AUTHOR]

Abstract: Visible–near infrared reflectance (NIR) spectroscopy was applied to identify the subtle bruise of fresh jujube. In this paper, 70 samples were selected randomly as a calibration set and 30 samples were selected as a prediction set. Multiplicative scatter calibration (MSC) was used to pre-process the spectra, 9 characteristic wavelengths were selected by successive projection algorithm (SPA) in the end. On the basis of spectroscopy theoretical analysis, the principal component analysis (PCA) was performed to analyze the spectral data of the characteristic wavelengths, and 4 principal components (PCs) data which were used as the input variables for the Least squares support vector machine (LS–SVM) model for identifying the subtle bruise of fresh Jujube fruits were picked up. Intact jujubes and bruise jujubes in prediction with code 1 and 2 respectively could be considered as the outputs. The identification accuracy rate of the MSC–SPA–PCA–LS–SVM model for subtle bruises of fresh jujube fruits could reach 100%, proving that the key information in spectral data could be effectively picked up by SPA and PCA combination methods. It is feasible to identify the subtle bruise of fresh jujube using visible–near infrared reflectance (NIR) spectroscopy. [Copyright &y& Elsevier]