Determination of acid value during edible oil storage using a portable <scp>NIR</scp> spectroscopy system combined with variable selection algorithms based on an <scp>MPA</scp>‐based strategy

Jiang, Hui; He, Yingchao; Chen, Quansheng

doi:10.1002/jsfa.10962

Cited by 19 publications

(7 citation statements)

References 33 publications

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“…Moisture content plays an important role in reflecting the quality and stability of foods and TCMs [ 17 , 18 ]. Storage conditions with high moisture content are beneficial for the growth of microbes, leading to the production of more free fatty acids and even unpleasant odor [ 19 , 20 , 21 ].…”

Section: Resultsmentioning

confidence: 99%

Comprehensive Quality Evaluation for Medicinal and Edible Ziziphi Spinosae Semen before and after Rancidity Based on Traditional Sensory, Physicochemical Characteristics, and Volatile Compounds

Liu

Song

et al. 2022

Foods

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To comprehensively evaluate the quality of medicinal and edible Ziziphi Spinosae Semen (ZSS, the dried ripe seeds of Ziziphus jujuba var. spinosa) before and after rancidity during storage, some indicators including traditional sensory properties, physicochemical characteristics, and volatile compounds were analyzed. As a result, compared with the normal samples, the rancid samples of ZSS produced a darker color, a bitter taste, and an irritating odor, increased moisture content, electrical conductivity, fatty oil content, and acid value, and decreased water- and alcohol-soluble extract contents and pH value. Among them, the acid value had significant difference (p < 0.01) from 3.90 of normal ZSS to 18.68 mg/g of rancid ZSS. A total of 39 volatile compounds were identified in samples, including 20 in normal ZSS and 38 compounds in rancid ZSS. Nineteen common compounds were identified in normal and rancid samples. Among them, the content of 10 compounds such as δ-limonene, (R,R)-2,3-butanediol, and (R,S)-2,3-butanediol was decreased but that of nine compounds such as acetic acid, n-octanoic acid, and n-nonanoic acid was increased in rancid ZSS. Nineteen unique compounds such as β-phellandrene, α-pinene, and 3-carene were detected and only one compound, δ-cadinene, was not detected in rancid ZSS. In addition, eight short-chain organic acids, acetic, propanoic, butanoic, pentanoic, hexanoic, heptanoic, octanoic, and nonanoic acids, were new products in rancid ZSS, and it was speculated that the production of a series of organic acids might be the material basis of irritating odor after normal ZSS became rancid. This is the first report that a series of short-chain organic acids have been found in a rancid substance. In conclusion, there was a significant difference between normal and rancid ZSS. These indicators could be used as an early warning for judging the rancidity phenomenon of medicinal and edible ZSS. In addition, this is the first comprehensive evaluation about the rancidity process of a medicinal and edible substance.

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Section: Resultsmentioning

confidence: 99%

Comprehensive Quality Evaluation for Medicinal and Edible Ziziphi Spinosae Semen before and after Rancidity Based on Traditional Sensory, Physicochemical Characteristics, and Volatile Compounds

Liu

Song

et al. 2022

Foods

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“…An atmospheric compensation of ethanol was combined with the PCA method by feature extraction to identify different brands of goods, as well as to assess the degree of food spoilage. Hui et al [ 51 ] used a portable NIR spectroscopy system to determine the acidity of edible oils during storage and compared four variable selection methods (CARS, VISSA, IVSO, and BOSS) to optimize the NIR spectral data, and obtained support from vector machine models for different selection methods. The BOSS method yielded the lowest number of characteristic wavelengths.…”

Section: Application Of Infrared Spectroscopy Technology In Detection...mentioning

confidence: 99%

Research Progress of Applying Infrared Spectroscopy Technology for Detection of Toxic and Harmful Substances in Food

Tian

et al. 2022

Foods

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In recent years, food safety incidents have been frequently reported. Food or raw materials themselves contain substances that may endanger human health and are called toxic and harmful substances in food, which can be divided into endogenous, exogenous toxic, and harmful substances and biological toxins. Therefore, realizing the rapid, efficient, and nondestructive testing of toxic and harmful substances in food is of great significance to ensure food safety and improve the ability of food safety supervision. Among the nondestructive detection methods, infrared spectroscopy technology has become a powerful solution for detecting toxic and harmful substances in food with its high efficiency, speed, easy operation, and low costs, while requiring less sample size and is nondestructive, and has been widely used in many fields. In this review, the concept and principle of IR spectroscopy in food are briefly introduced, including NIR and FTIR. Then, the main progress and contribution of IR spectroscopy are summarized, including the model’s establishment, technical application, and spectral optimization in grain, fruits, vegetables, and beverages. Moreover, the limitations and development prospects of detection are discussed. It is anticipated that infrared spectroscopy technology, in combination with other advanced technologies, will be widely used in the whole food safety field.

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“…From this, it is shown that the machine learning algorithm represented by SVM performs better than the PLSR algorithm to predict the alpha-pinene content in nutmeg fruits using Vis-NIR spectra data. If it is observed from the RPD parameters in alpha-pinene estimation modeling using Vis-NIR spectral data, it has been better than the model built by Jiang et al [32] to estimate the acid value of oil using portable NIRs.…”

Section: Calibration Model For Prediction Alpha-pinenementioning

confidence: 99%

A Vis-NIRs Calibration Model for the Prediction of Myristicin and Alpha-Pinene on Nutmeg: A Comparison Study of PLSR Algorithm and Machine Learning Algorithm

Munawar¹,

Sufardi²,

Yusmanizar³

et al. 2022

MMEP

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Determination of the myristicin and alpha-pinene content of nutmeg is still constrained by the extended testing time in the laboratory, which is expensive and is carried out destructively. In addition, non-destructive testing using spectroscopy often faces problems in building models that only rely on algorithms that perform linearly, such as PCR and PLSR. Therefore, the present study studied Vis-NIR (381-1065 nm) as a fast, inexpensive, and non-destructive mechanism to determine the myristicin and alpha-pinene of nutmeg fruits from Aceh, Indonesia. Two algorithms commonly used in spectral data processing, partial least squares regression (PLSR) and machine learning represented by a support vector machine (SVM), were employed and compared to predict myristicin and alpha-pinene in nutmeg fruits. The chemical reference parameters (myristicin and alpha-pinene) were measured using gas chromatography mass spectrometry (GC-MS). Standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing were involved as spectra enhancement before the prediction models outcome. The results show that the kernel of the radial basis function (RBF) kernel n-SVM algorithm is better than PLSR for myristicin prediction with gamma (g), c, and nu (n) of 0.1, 1.0, and 0.99, respectively. Also, the e-SVM algorithm by RBF kernel is better than PLSR for the prediction of alpha-pinene in nutmeg fruits with gamma (g), c, and epsilon (e) compositions of 0.01, 10, 0.1, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (Rp2), the root means square error of calibration (RMSEC) and prediction (RMSEP), and the ratio (RPD) for the prediction of myristicin were 0.992, 0.986, 0.941%, 1.325% and 8.348, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (Rp2), the root mean square error of calibration (RMSEC) and prediction (RMSEP), and the ratio of prediction to deviation ratio (RPD) for the prediction of alpha-pinene were 0.976, 0.979, 0.305%, 0.317% and 6.826, respectively. In general, the results satisfactorily indicate that Vis-NIRS, with the appropriate algorithm, has promising results in determining myristicin and alpha-pinene on nutmeg from Aceh, Indonesia, as nondestructive measurement.

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Determination of acid value during edible oil storage using a portable NIR spectroscopy system combined with variable selection algorithms based on an MPA‐based strategy

Cited by 19 publications

References 33 publications

Comprehensive Quality Evaluation for Medicinal and Edible Ziziphi Spinosae Semen before and after Rancidity Based on Traditional Sensory, Physicochemical Characteristics, and Volatile Compounds

Comprehensive Quality Evaluation for Medicinal and Edible Ziziphi Spinosae Semen before and after Rancidity Based on Traditional Sensory, Physicochemical Characteristics, and Volatile Compounds

Research Progress of Applying Infrared Spectroscopy Technology for Detection of Toxic and Harmful Substances in Food

A Vis-NIRs Calibration Model for the Prediction of Myristicin and Alpha-Pinene on Nutmeg: A Comparison Study of PLSR Algorithm and Machine Learning Algorithm

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