Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods

Río-Celestino, Mercedes Del; Font, R.

doi:10.3390/s22134845

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Due to the fact that bioactive components contain large amounts of the above-mentioned hydrogen-containing groups, NIR information can be associated with these chemical parameters to develop a quantitative analytical model. At present, NIR spectroscopy technology is widely used in agriculture [ 14 , 15 , 16 ], food [ 11 , 17 , 18 , 19 ], medicine [ 20 , 21 , 22 ], petrochemistry [ 23 , 24 , 25 ], and other fields. There are numerous studies confirming that NIR has good potential to successfully predict the antioxidant activity of Chinese medicine.…”

Section: Introductionmentioning

confidence: 99%

Rapid Measurement of Antioxidant Properties of Dendrobium officinale Using Near-Infrared Spectroscopy and Chemometrics

Cao,

Huang,

Chen

et al. 2024

Foods

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Dendrobium officinale (D. officinale), often used as a dual-use plant with herbal medicine and food applications, has attracted considerable attention for health-benefiting components and wide economic value. The antioxidant ability of D. officinale is of great significance to ensure its health care value and safeguard consumers’ interests. However, the common analytical methods for evaluating the antioxidant ability of D. officinale are time-consuming, laborious, and costly. In this study, near-infrared (NIR) spectroscopy and chemometrics were employed to establish a rapid and accurate method for the determination of 2,2′-azinobis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) scavenging capacity, 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity, and ferric reducing antioxidant power (FRAP) in D. officinale. The quantitative models were developed based on the partial least squares (PLS) algorithm. Two wavelength selection methods, namely the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) method, were used for model optimization. The CARS-PLS models exhibited superior predictive performance compared to other PLS models. The root mean square errors of cross-validation (RMSECVs) for ABTS, FRAP, and DPPH were 0.44%, 2.64 μmol/L, and 2.06%, respectively. The results demonstrated the potential application of NIR spectroscopy combined with the CARS-PLS model for the rapid prediction of antioxidant activity in D. officinale. This method can serve as an alternative to conventional analytical methods for efficiently quantifying the antioxidant properties in D. officinale.

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Section: Introductionmentioning

confidence: 99%

Rapid Measurement of Antioxidant Properties of Dendrobium officinale Using Near-Infrared Spectroscopy and Chemometrics

Cao,

Huang,

Chen

et al. 2024

Foods

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show abstract

“…Near-infrared (NIR) spectroscopy is an ideal choice due to its benefits such as fast analysis speed, high accuracy, easy operation, absence of additional reagents, and environmental friendliness [14]. It is now extensively utilized in various fields, including pharmaceuticals, petrochemicals, textiles, cosmetics, and medical applications [15]. NIR is a type of light that falls between the visible range and the mid-infrared range.…”

Section: Introductionmentioning

confidence: 99%

Rapid Measurement of Total Saponins, Mannitol, and Naringenin in Dendrobium officinale by Near-Infrared Spectroscopy and Chemometrics

She,

Huang,

Cao

et al. 2024

Foods

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Dendrobium officinale has drawn increasing attention as a dual-use plant with herbal medicine and food applications. The efficient quality evaluation of D. officinale is essential to ensuring its nutritional and pharmaceutical value. Given that traditional analytical methods are generally time-consuming, expensive, and laborious, this study developed a rapid and efficient approach to assess the quality of D. officinale from different geographical origins by near-infrared (NIR) spectroscopy and chemometrics. Total saponins, mannitol, and naringenin were utilized as quality indicators. Two wavelength selection methods, namely, uninformative variable elimination and competitive adaptive reweighted sampling (CARS), were utilized to enhance the prediction accuracy of the quantification model. Moreover, multiple spectral pretreatment methods were applied for model optimization. Results indicated that the partial least squares (PLS) model constructed based on the wavelengths selected by CARS exhibited superior performance in predicting the contents of the quality indicators. The coefficient of determination (RP2) and root mean square error (RMSEP) in the independent test sets were 0.8949 and 0.1250 g kg−1 for total saponins, 0.9664 and 0.2192 g kg−1 for mannitol, and 0.8570 and 0.003159 g kg−1 for naringenin, respectively. This study revealed that NIR spectroscopy and the CARS-PLS model could be used as a rapid and accurate technique to evaluate the quality of D. officinale.

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“…Hyperspectral images are images that contain information about the spectral reflectance or emission of materials at multiple wavelengths. Each technique offers special advantages and applications, contributing to a wide range of fields, such as agriculture, food safety, and remote sensing [20][21][22]. The hyperspectral imaging method to detect the microbial contamination of date palm fruits, using latent and quadratic discriminant analyses, was investigated in [23].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres

Mohammed,

Srinivasagan,

Alzahrani

et al. 2023

Sustainability

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The safety and quality of fresh fruits deserve the greatest attention, and are a priority for producers and consumers alike. Modern technologies are crucial to accurately estimating and predicting fresh fruits’ quality and shelf life, to optimize supply chain management. Modified atmosphere packaging (MAP) is an essential method that maintains quality parameters and increases the shelf life of fresh fruits by reducing their ripening rates. This study aimed to develop a cost-effective, non-destructive technique using tiny machine learning (TinyML) and a multispectral sensor to predict/estimate the quality parameters and shelf life of packaged fresh dates under the natural atmosphere (Control), vacuum-sealed bags (VSBs), and MAP with different gas combinations: 20% CO2 + N balance (MAP1), and 20% CO2 + 10% O2 + N balance (MAP2). The shelf life and quality parameters of the packaged fresh dates (pH, total soluble solids (TSSs), sugar content (SC), moisture content (MC), and tannin content (TC)) were evaluated under different storage temperatures and times. A multispectral sensor (AS7265x) was utilized to correlate the fruit quality parameters with spectrum analysis under the same storage conditions, to prepare the dataset to train the prediction models. The prediction models were trained in the Edge Impulse Platform, and deployed to an Arduino Nano 33 BLE sense microcontroller unit (MCU) for inference. The findings indicated that the vacuum and MAP1 efficiently increased the shelf life and maintained the quality parameters of the packaged fresh fruit to 43 ± 2.39 and 39 ± 3.34 days, respectively, at 5 °C. The optimal neural network consisted of the input layer with 20 nodes (the packaging type, storage temperature, and 18 channels of the spectral sensor data at 410 to 940 nm wavelengths), two hidden layers with 20 and 12 nodes, and an output layer with one node for the target quality parameter or shelf life. These optimal prediction models efficiently predicted the shelf life with R2 = 0.951, pH with R2 = 0.854, TSSs with R2 = 0.893, SC with R2 = 0.881, MC with R2 = 0.941, and TC with R2 = 0.909. The evaluation of the developed prediction models under each packaging condition indicated that these models serve as powerful tools for accurately predicting fruit quality parameters, and estimating the shelf life of fresh dates.

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Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods

Abstract: Over the past four decades, near-infrared reflectance spectroscopy (NIRS) has become one of the most attractive and used technique for analysis as it allows for fast and simultaneous qualitative and quantitative characterization of a wide variety of food samples [...]

Cited by 6 publications

References 22 publications

Rapid Measurement of Antioxidant Properties of Dendrobium officinale Using Near-Infrared Spectroscopy and Chemometrics

Rapid Measurement of Antioxidant Properties of Dendrobium officinale Using Near-Infrared Spectroscopy and Chemometrics

Rapid Measurement of Total Saponins, Mannitol, and Naringenin in Dendrobium officinale by Near-Infrared Spectroscopy and Chemometrics

Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres

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