Present and future of portable/handheld near-infrared spectroscopy in chicken meat industry

Pérez, Irene Marivel Nolasco; Cruz-Tirado, J.P.; Badaró, Amanda Teixeira; Oliveira, Marciano M.; Barbin, Douglas Fernandes

doi:10.1177/0960336019861476

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…To speed up and simplify the process of determining meat quality, a portable or handheld NIR device can be built that can be taken anywhere [9] [10]. The development of a portable NIR device can be applied in industries that require the process of determining the characteristics of meat products [11] [12].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Model Development on Near-Infrared Spectroscopy Data

Raafi’udin,

Purwanto,

Sitanggang

et al. 2024

IJACSA

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This study aims to develop a feature selection model on Near-Infrared Spectroscopy (NIRS) data. The object used is beef with six quality parameters: color, drip loss, pH, storage time, Total Plate Colony (TPC), and water moisture. The prediction model is a Random Forest Regressor (RFR) with default parameters. The feature selection model is carried out by mapping spectroscopic data into line form. The collection of lines is made into one line by finding the mean value. Next, apply the line simplification method based on angle elimination, starting from the smallest angle to the largest. Each iteration will eliminate one corner, reducing one column of data in the corresponding dataset. Then, the predicted value in the form of R2 will be collected, and the highest value will be considered the best feature selection formation. RFR prediction results with R2 values are as follows: color R2= 0.597, drip loss R2=0.891, pH R2=0.797, storage time R2=0.889, TPC R2=0.721, and water moisture R2=0.540. Meanwhile, after applying the feature selection model, the R2 values for all parameters increased to color R2=0.877, drip loss R2=0.943, pH R2=0.904, storage time R2=0.917, TPC R2=0.951, and water moisture R2=0.893. Based on the results of increasing the R2 value of the six parameters, an average value of increasing prediction accuracy of 17.49% can be taken. So, the feature selection method based on line simplification with an angle elimination system can provide very good results.

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Section: Introductionmentioning

confidence: 99%

Feature Selection Model Development on Near-Infrared Spectroscopy Data

Raafi’udin,

Purwanto,

Sitanggang

et al. 2024

IJACSA

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“…Advancements in chemometrics and instrumental measurement techniques have increasingly highlighted the application of near-infrared (NIR) spectroscopy analysis in scientific research and industrial applications because of its nondestructive, rapid, and precise nature. Its widespread application spans various sectors, including food science [1], environmental chemistry [2], daily chemical industry [3], traditional Chinese medicine [4], polymer materials [5], petroleum, and biochemistry [6].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Near-Infrared Spectroscopy Using the BEST-1DConvNet Model

Li,

Deng

2024

Processes

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In the quest for enhanced precision in near-infrared spectroscopy (NIRS), in this study, the application of a novel BEST-1DConvNet model for quantitative analysis is investigated against conventional support vector machine (SVM) approaches with preprocessing such as multiplicative scatter correction (MSC) and standard normal variate (SNV). We assessed the performance of these methods on NIRS datasets of diesel, gasoline, and milk using a Fourier Transform Near-Infrared (FT-NIR) spectrometer having a wavelength range of 900–1700 nm for diesel and gasoline and 4000–10,000 nm for milk, ensuring comprehensive spectral capture. The BEST-1DConvNet’s effectiveness in chemometric predictions was quantitatively gauged by improvements in the coefficient of determination (R2) and reductions in the root mean square error (RMSE). The BEST-1DConvNet model achieved significant performance enhancements compared to the MSC + SNV + 1D + SVM model. Notably, the R2 value for diesel increased by approximately 48.85% despite a marginal RMSE decrease of 0.92%. R2 increased by 11.30% with a 3.32% RMSE reduction for gasoline, and it increased by 8.71%, accompanied by a 3.51% RMSE decrease for milk. In conclusion, the BEST-1DConvNet model demonstrates superior predictive accuracy and reliability in NIRS data analysis, marking a substantial leap forward in spectral analysis technology. This advancement could potentially streamline their integration into various industrial applications and highlight the role of convolutional neural networks in future chemometric methodologies.

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