Effect of variable selection algorithms on model performance for predicting moisture content in biological materials using spectral data

Kamruzzaman, Mohammed; Kalita, Diganta; Ahmed, Md Toukir; ElMasry, Gamal; Makino, Yoshio

doi:10.1016/j.aca.2021.339390

Cited by 55 publications

(20 citation statements)

References 65 publications

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“…In addition to that, WRC, SFS, and SPA selected a single wavelength in the 1400-1450 nm region. Several studies have proved that these two wavelength regions resemble the combination of the O-H bands associated with the water [35,51] present in the vegetables. However, both vegetables and FMs had some common peaks near the rest of the selected wavebands, but they had a clear difference in intensity.…”

Section: Selection Of Important Wavebandsmentioning

confidence: 99%

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Multispectral Wavebands Selection for the Detection of Potential Foreign Materials in Fresh-Cut Vegetables

Tunny

Amanah

Faqeerzada

et al. 2022

Sensors

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Ensuring the quality of fresh-cut vegetables is the greatest challenge for the food industry and is equally as important to consumers (and their health). Several investigations have proven the necessity of advanced technology for detecting foreign materials (FMs) in fresh-cut vegetables. In this study, the possibility of using near infrared spectral analysis as a potential technique was investigated to identify various types of FMs in seven common fresh-cut vegetables by selecting important wavebands. Various waveband selection methods, such as the weighted regression coefficient (WRC), variable importance in projection (VIP), sequential feature selection (SFS), successive projection algorithm (SPA), and interval PLS (iPLS), were used to investigate the optimal multispectral wavebands to classify the FMs and vegetables. The application of selected wavebands was further tested using NIR imaging, and the results showed good potentiality by identifying 99 out of 107 FMs. The results indicate the high applicability of the multispectral NIR imaging technique to detect FMs in fresh-cut vegetables for industrial application.

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Section: Selection Of Important Wavebandsmentioning

confidence: 99%

“…However, the absorbance value from the original sample spectra needs to consider the corresponding WRC value to select the variables. Variables with a higher β-coefficient but lower (or no) peak in the sample spectra may not contribute to the model prediction [35].…”

Section: Waveband Selection Based On Wrcmentioning

confidence: 99%

Multispectral Wavebands Selection for the Detection of Potential Foreign Materials in Fresh-Cut Vegetables

Tunny

Amanah

Faqeerzada

et al. 2022

Sensors

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show abstract

“…Shi et al (2019) employed HSI to classify colonies from food fragments (sausage, bacon, and millet fragments) in an agar plate [ 23 ]. Kamrruzzaman et al (2022) studied the moisture of red meat and corn using six different methods to select the wavelength in terms of model performance and demonstrated that competitive adaptive reweighted sampling (CARS) in tandem with PLSR is superior to PLSR models when developing models between the moisture and spectra of red meat and corn using a full spectral range [ 24 ]. Siripatrawan and Makino (2018) evaluated the changes in physicochemical, microbiological, and sensory attributes of packaged bratwurst (a type of sausage) during 20-day storage at 4 ± 1 °C [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of pH in Sausages Stuffed in a Modified Casing with Orange Extracts by Hyperspectral Imaging Coupled with Response Surface Methodology

Feng

Arai

Rodríguez-Pulido

2022

Foods

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The pH values of sausages stuffed in natural hog casings with different modifications (soy lecithin, soy oil, orange extracts (OE) from waste orange peels, lactic acid in slush salt, and treatment time) after 16-day 4 °C storage were evaluated for the first time by hyperspectral imaging (350–1100 nm) coupled with response surface methodology (RSM). A partial least squares regression (PLSR) model was developed to relate the spectra to the pH of sausages. Spectral pretreatment, including first derivative, second derivative, multiplicative scatter correction (MSC), standard normal variate (SNV), normalization, and normalization, with different combinations was employed to improve model performance. RSM showed that only soy lecithin and OE interactively affected the pH of sausages (p < 0.05). The pH value decreased when the casing was treated with a higher concentration of soy lecithin with 0.26% OE. As the first and second derivatives are commonly used to eliminate the baseline shift, the PLSR model derived from absorbance pretreated by the first derivative in the full wavelengths showed a calibration coefficient of determination (R2) of 0.73 with a root mean square error of calibration of 0.4283. Twelve feature wavelengths were selected with a comparable R2 value compared with the full wavelengths. The prediction map enables the visualization of the pH evolution of sausages stuffed in the modified casings added with OE.

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“…e whole procedure of feature selection is to associate the selected variables with the property of interest, which many have done successfully such as regression coefficient (RC) [17], variable importance in projection (VIP), and the interval PLS (iPLS) [18]. Moreover, elimination of collinearity between variables is also noted by some methods such as successive projection algorithm (SPA), principal component analysis (PCA) loadings [19].…”

Section: Introductionmentioning

confidence: 99%

“…Variable Selection Based on CARS. CARS[12,17] is a new and novel variable selection method based on PLSR and "survival of the fittest," the principle of Darwin's eory of Evolution. e main feature of this algorithm is the calculation of PLS regression coefficients and reweighted sampling.…”

mentioning

confidence: 99%

A Variable Selection Method Based on Fast Nondominated Sorting Genetic Algorithm for Qualitative Discrimination of Near Infrared Spectroscopy

Liu

Yuan

et al. 2022

Journal of Spectroscopy

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A reliable and effective qualitative near-infrared (NIR) spectroscopy discrimination method is critical for excellent model building, yet the performance of models built by these methods is highly dependent on valid feature extraction. The goal of feature selection is to associate the selected variables with the property of interest, which many have done successfully. However, many of selection methods focus only on strong association with the analytes or properties of interest, neglecting correlations between variables. A variable selection method based on a fast nondominated-ranking genetic algorithm (NSGA-II) was proposed in this paper for qualitative discrimination of NIR spectra. The method had two objective functions: (1) maximizing the sum of ratios of interclass variance to intraclass variance, (2) minimizing the sum of correlation coefficients between the selected variables. FT-NIR spectra of a total of 124 tobacco samples from different origins and parts in Guizhou Province, China, were used as the experimental objects, and the part-grade discrimination models of tobacco leaves were established by combining this method with partial least squares-based discriminant analysis (PLS-DA), and compared with PLS-DA model based on the full spectrum. The results showed that the performance of PLS-DA model with the NSGA-II was improved, with a comparable or better correct discrimination rate and reasonable discrimination rate, and could discriminate different parts of the tobacco leaves well. It indicates that the NSGA-II can select a few and effective feature variables to build a high-performance qualitative discrimination model and is proved to be a promising algorithm. In addition, the method is not designed exclusively for spectral data. It is a general strategy that could be used for variable selection for other types of data.

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Effect of variable selection algorithms on model performance for predicting moisture content in biological materials using spectral data

Cited by 55 publications

References 65 publications

Multispectral Wavebands Selection for the Detection of Potential Foreign Materials in Fresh-Cut Vegetables

Multispectral Wavebands Selection for the Detection of Potential Foreign Materials in Fresh-Cut Vegetables

Evaluation of pH in Sausages Stuffed in a Modified Casing with Orange Extracts by Hyperspectral Imaging Coupled with Response Surface Methodology

A Variable Selection Method Based on Fast Nondominated Sorting Genetic Algorithm for Qualitative Discrimination of Near Infrared Spectroscopy

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