Rancidity Estimation of Perilla Seed Oil by Using Near-Infrared Spectroscopy and Multivariate Analysis Techniques

Hong, Suk-Ju; Rho, Shin‐Joung; Lee, Ah-Yeong; Park, Heesoo; Cui, Jinshi; Park, Jong-Min; Hong, Soon-Jung; Kim, Yong‐Ro; Kim, Ghiseok

doi:10.1155/2017/1082612

Cited by 14 publications

(7 citation statements)

References 18 publications

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“…PLS regression is a multivariate analysis technique that generalizes and combines features based on principal component analysis and multiple regression [ 36 ]. It is widely used as a multivariate calibration method for processing large amounts of data to predict the behavior of dependent variables based on large datasets of independent variables [ 37 , 38 ]. The PLSR model depends on consideration of the X and Y variables in a designed matrix, in which the linear relationship between the X and Y variables enables the model to predict the components in the X variables [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method

Faqeerzada

Lohumi

Kim

et al. 2020

Sensors

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The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89–100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R2) and low error values (<1%) for each variety of almond powder adulterated with apricot; however, a relatively higher error rates of 2.5% and 4.4% for the two varieties of almond powder adulterated with peanut powder, which indicates the performance of quantitative analysis model could vary with sample condition, such as variety, originality, etc. PLSR-based concentration mapped images visually characterized the adulterant (apricot) concentration in the almond powder. These results demonstrate that the SWIR-HSI technique combined with the one-class classifier DD-SIMCA can be used effectively for a high-throughput quality screening of almond powder regarding potential adulteration.

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Section: Methodsmentioning

confidence: 99%

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method

Faqeerzada

Lohumi

Kim

et al. 2020

Sensors

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“…Sep. 2017, [85] The rancidity of perilla oil ANN multivariate analysis methods ANN models produced the best prediction results.…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

A Review of Machine Learning for Near-Infrared Spectroscopy

Zhang

Kasun

Wang

et al. 2022

Sensors

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The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.

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“…Typically, peroxide values increased with an increment storage time of oils. The fatty acid composition also affected the oxidation rate and rancidity of the oil [37]. In general, increasing the amount of linolenic acid and/or decreasing the level of oleic acid caused a decrease in the oxidative stability of the oils [38].…”

Section: Plant Sterols Of P Frutescens M Oleifera and Mixed Seed Oilmentioning

confidence: 99%

Emulgels Containing Perilla frutescens Seed Oil, Moringa oleifera Seed Oil, and Mixed Seed Oil: Microemulsion and Safety Assessment

et al. 2022

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P. frutescens seed oil and M. oleifera seed oil consist of fatty acids and sterols that are beneficial for skin. Mixing of these oils at 1:1 ratio has shown to increase antioxidant activity of oils. This study aims to formulate emulgels containing microemulsions of P. frutescens seed oil, M. oleifera seed oil, and mixed P. frutescens and M. oleifera seed oils. The chemical constituents of P. frutescens seed oil, M. oleifera seed oil, and mixed seed oil are analyzed by gas chromatography/mass spectrometry (GC/MS). The microemulsions are formulated by a phase titration method and characterized for the droplet size, polydispersity index, and zeta potential value using a dynamic light scattering technique. The physical and chemical stability of the microemulsions are investigated using a rheometer and UV-Visible spectrophotometer, respectively. The safety of microemulsion is evaluated on PBMC and human subjects. Emulgels containing three different types of microemulsion are formulated. The results show that P. frutescens seed oil is mainly composed of alpha-linolenic acid, linoleic acid, and oleic acid, whereas M. oleifera seed oil contains a high proportion of oleic acid. Mixed seed oil contains a comparable amount of alpha-linolenic acid and oleic acid. All types of oils are composed of β-sitosterol as the major plant sterol. Microemulsions of all types of oils are successfully prepared by using Tween 80 as a surfactant due to the largest transparent region of pseudoternary phase diagram. The size, polydispersity index, and zeta potential values of all types of microemulsion are in the acceptable range upon storage at 30 °C for 1 month. Microemulsions exhibit pseudoplastic flow behavior. The percent of remaining oils in all types of microemulsion is more than 90% after storage at 30 °C for 1 month. Emulgels containing three types of microemulsions exhibit good characteristics and no change in viscosity after storage at 4, 30, and 45 °C for 1 month. The safety results reveal that three types of microemulsion do not induce cytotoxicity to PBMC nor induce skin irritation and allergic reactions. Emulgels containing microemulsions developed in this study can be used to safely deliver P. frutescens seed oil, M. oleifera seed oil, and mixed seed oil to human skin.

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Rancidity Estimation of Perilla Seed Oil by Using Near-Infrared Spectroscopy and Multivariate Analysis Techniques

Cited by 14 publications

References 18 publications

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method

A Review of Machine Learning for Near-Infrared Spectroscopy

Emulgels Containing Perilla frutescens Seed Oil, Moringa oleifera Seed Oil, and Mixed Seed Oil: Microemulsion and Safety Assessment

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