Mid-infrared spectroscopy and support vector machines applied to control the hydrogenation process of soybean oil

Adulteration is one of the major concerns among all the quality problems of milk powder. Soybean flour and rice flour are harmless adulterations in the milk powder. In this study, mid-infrared spectroscopy was used to detect the milk powder adulterated with rice flour or soybean flour and simultaneously determine the adulterations content. Partial least squares (PLS), support vector machine (SVM) and extreme learning machine (ELM) were used to establish classification and regression models using full spectra and optimal wavenumbers. ELM models using the optimal wavenumbers selected by principal component analysis (PCA) loadings obtained good results with all the sensitivity and specificity over 90%. Regression models using the full spectra and the optimal wavenumbers selected by successive projections algorithm (SPA) obtained good results, with coefficient of determination (R2) of calibration and prediction all over 0.9 and the predictive residual deviation (RPD) over 3. The classification results of ELM models and the determination results of adulterations content indicated that the mid-infrared spectroscopy was an effective technique to detect the rice flour and soybean flour adulteration in the milk powder. This study would help to apply mid-infrared spectroscopy to the detection of adulterations such as rice flour and soybean flour in real-world conditions.

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Total and Hot-Water Extractable Organic Carbon and Nitrogen in Organic Soil Amendments: Their Prediction Using Portable Mid-Infrared Spectroscopy with Support Vector Machines

Agronomy ◽

10.3390/agronomy11040659 ◽

2021 ◽

Vol 11 (4) ◽

pp. 659

Author(s):

Ralf Wehrle ◽

Gerhard Welp ◽

Stefan Pätzold

Keyword(s):

Particle Size ◽

Infrared Spectroscopy ◽

Prediction Models ◽

Organic Amendments ◽

Hot Water ◽

Support Vector ◽

Mid Infrared ◽

Carbon And Nitrogen ◽

Vector Machines ◽

Water Extractable

Against the background of climate change mitigation, organic amendments (OA) may contribute to store carbon (C) in soils, given that the OA provide a sufficient stability and resistance to degradation. In terms of the evaluation of OA behavior in soil, total organic carbon (TOC), total nitrogen (TN), and the ratio of TOC to TN (CN-ratio) are important basic indicators. Hot-water extractable carbon (hwC) and nitrogen (hwN) as well as their ratios to TOC and TN are appropriate to characterize a labile pool of organic matter. As for quickly determining these properties, mid-infrared spectroscopy (MIRS) in combination with calibrations based on machine learning methods are potentially capable of analyzing various OA attributes. Recently available portable devices (pMIRS) might replace established benchtop devices (bMIRS) as they have potential for on-site measurements that would facilitate the workflow. Here, we used non-linear support vector machines (SVM) to calibrate prediction models for a heterogeneous dataset of greenwaste composts and biochar compost substrates (BCS) (n = 45) using bMIRS and pMIRS instruments on ground samples. Calibrated models for both devices were validated on separate test sets and showed similar results. Ten OA were sieved to particle size classes (psc’s) of >4 mm, 2–4 mm, 0.5–2 mm, and <0.5 mm. A universal SVM model was then developed for all OA and psc’s (n = 162) via pMIRS. Validation revealed that the models provided reliable predictions for most parameters (R2 = 0.49–0.93; ratio of performance to interquartile distance (RPIQ) = 1.19–5.70). We conclude that (i) the examined parameters are sensitive towards chemical composition of OA as well as particle size distribution and can therefore be used as indicators for labile carbon and nitrogen pools of OA, (ii) prediction models based on SVM and pMIRS are a feasible approach to predict the examined C and N pools in organic amendments and their particle size class, and (iii) pMIRS can provide valuable information for optimized application of OA on cultivated soils at low costs and efforts.

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