The potency of Vis/NIR spectroscopy for classification of soybean based of colour

Pahlawan, M F R; Murti, Betty M. A.; Masithoh, Rudiati Evi

doi:10.1088/1755-1315/1018/1/012015

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…Longoni et al (2020) and Gebregziabher et al (2022) reported chlorophyll and carotenes substances in the visible range, where R 2 values in calibration and prediction played a significant role in specific sample classification. At 680 nm, soybean samples showed chlorophyll content that distinguished green color (Pahlawan et al (2022). In the visible range, the absorption peaks of chlorophyll-a occur at 430 nm and 662 nm, while chlorophyll-b was found at 453 nm and 642 nm ( Lopes et al 2017;Shi et al 2022;Yang et al 2022;Zhu et al 2018).…”

Section: Partial Least Square Regression (Plsr)mentioning

confidence: 99%

“…Peak in absorbance of around 670 nm found in the olive oil spectrum confirmed the presence of chlorophyll pigments (Lapčíková et al 2018). At 500 nm, soybean samples indicated carotenoid content that distinguished yellow or darker (Pahlawan et al 2022).…”

Section: Partial Least Square Regression (Plsr)mentioning

confidence: 99%

“…Vis-NIR spectrometer has been extensively investigated, emphasizing its suitability for field applications (Walsh et al 2020) due to simplicity, speed, and cost-effectiveness for quality evaluation (Cortés et al 2019) and classification (Monago-Maraña et al 2021). Specifically, fiber optic-based Vis-NIR spectroscopy has been explored for soybean characterization (Abadi et al 2022), showing good performance for classification (Pahlawan et al 2022), water evaluation (Pahlawan et al 2023), and authentication (Masithoh et al 2023). The results showed the potential of this instrument to support the production process for soybean characterization in field applications.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Indonesian Local Soybean Based on Chemical Characteristics and Visible - Near Infrared Spectra With Chemometrics

Masithoh,

Abadi,

Sutiarso

et al. 2024

BIOTROPIA

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Soybean characterization is essential to ensure product quality during distribution according to internal values. In this context, non-destructive characterization method, such as spectroscopy, offer an effective and efficient approach to testing soybean quality in field applications. Among the instruments that are widely used for testing soybean quality, the semi-portable visible near-infrared (Vis-NIR) spectrometer operating at a specific range of 345 to 1033 nm has been proven effective. Therefore, this study aimed to investigate soybean seeds characterization using Vis-NIR spectroscopy with PCA and PLSR chemometric methods. The investigation was carried out using soybean seeds consisting of eight varieties locally produced on Java Island, Indonesia, including Dega1, Dena1, Deja2, Dering1, Devon1, Yellow Flap, Green, and Detam4, in the form of intact, crumble, flour, and paste. Several quality parameters such as protein, fat, crude fiber, carbohydrate, ash, water, chlorophyll, total carotene, vitamin C, and L*, a*, and b* values were measured across intact, crumble, flour, and paste samples. The results of Principal Component Analysis (PCA) showed that sample form and genotypes affected soybean classification. Furthermore, Partial Least Squares Regression (PLSR) showed adequate model calibration for crude fiber, chlorophyll, total carotene, and vitamin C parameters. Based on this analysis, it could be concluded that Vis-NIR spectroscopy proved to be suitable for the classification and prediction of soybean characterization.

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Section: Partial Least Square Regression (Plsr)mentioning

confidence: 99%

Section: Partial Least Square Regression (Plsr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Indonesian Local Soybean Based on Chemical Characteristics and Visible - Near Infrared Spectra With Chemometrics

Masithoh,

Abadi,

Sutiarso

et al. 2024

BIOTROPIA

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“…Statistical techniques are used to extract information from the spectra. Vis/NIR spectroscopy is a non-destructive technique used to measure quality attributes of fruits and vegetables [4].…”

Section: Introductionmentioning

confidence: 99%

Vis/NIR reflectance spectroscopy for non-destructive diagnosis of Fusarium spp. infection in postharvest potato tubers (Solanum tuberosum)

Prasetyo,

Amanah,

Farras

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

Self Cite

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Conventional methods of detecting Fusarium spp. infection, which causes significant economic losses in potato production, are time-consuming and expensive. This study explored rapid and non-destructive detection techniques using visible/near-infrared (Vis/NIR) spectroscopy. Potato seeds of the Granola L variety were intentionally infected with Fusarium spp. by fungal inoculation, then stored at 12°C, 25°C, and a combination of both. Healthy potatoes were stored under the same conditions in containers for 30 days and monitored every five days. Principal component analysis-linear discriminate analysis (PCA-LDA) was used to classify potato tubers based on their infection status. PCA-LDA analysis revealed significant spectral differences between healthy and infected potato seeds across all storage temperatures. Calibration reliability values were 95.87% (for samples stored at 12°C), 97.52% (stored at 25°C), and 98.35% (for the combination of 12°C and 25°C). Similar trends were observed for accuracy: 91.96% (12°C), 98.29% (25°C), and the highest accuracy of 98.65% for the combined temperature. These techniques facilitate rapid identification of infections, aiding farmers and producers in implementing more efficient preventive actions, resulting in decreased crop losses and waste products and enhanced productivity in the agricultural sector.

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“…Meanwhile, a Vis-NIR spectroscopy working at the wavelength of 350 -1000 nm is affordable and has been used to predict the contents of lycopene and carotene in tomatoes [21] or chlorophyl in apples [22]. The portable type of Vis-NIR spectrometer is cheap and suitable for the quality [23,24] and quantity detection [25,26] of agricultural products. However, the use of a Vis-NIR spectroscopy for the quantification of phytochemicals in red beet has not been studied.…”

Section: Introductionmentioning

confidence: 99%

Determination of the Betacyanin and Betaxanthin Contents of Red Beet (Beta Vulgaris) Powder Using Partial Least Square Regression Based on Visible-Near Infrared Spectra

Masithoh,

Pahlawan,

Arifani

et al. 2024

Trends Sci

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Red beet (Beta vulgaris) contains betalain, which comprises red-violet betacyanin and yellow betaxanthin with esthetic and health benefits. Betacyanin and betaxanthin are usually detected using common chemical analysis, which requires a long time, skilled analysts, and sample destruction. For fast and accurate measurement, this study utilized a portable low-cost Visible-Near Infrared Spectra (Vis-NIR) spectrometer at 350 - 1000 nm combined with partial least square regression to predict the betacyanin and betaxanthin contents of red beet powder. The best calibration models for betacyanin and betaxanthin had R2c of 0.89 and 0.919, respectively, and standard error of calibration SEC of 0.108 and 0.037 mg/g, respectively. The models were able to predict the contents of both pigments with R2p of 0.87, standard error of prediction SEP of 0.108 mg/g, and the ratio of prediction to deviation RPD of 2.52 for betacyanin and R2p of 0.84, SEP of 0.056 mg/g, and RPD of 2.47 for betaxanthin. When applied to external unknown data, the models predicted the contents of betacyanin and betaxanthin with R2 of 0.98 and root mean square error of 0.107 and 0.055 mg/g. Moreover, the predicted values were not significantly different at 95 % confidence. HIGHLIGHTS Betacyanin and betaxanthin from red beet, were determined nondestructively using a portable Vis-NIR spectrometer, an affordable device for small food industries Betacyanin and betaxanthin regression coefficients showed peaks at 480 and 600 nm Betacyanin regression coefficients peak was also observed at 540 nm GRAPHICAL ABSTRACT

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The potency of Vis/NIR spectroscopy for classification of soybean based of colour

Cited by 6 publications

References 16 publications

Evaluation of Indonesian Local Soybean Based on Chemical Characteristics and Visible - Near Infrared Spectra With Chemometrics

Evaluation of Indonesian Local Soybean Based on Chemical Characteristics and Visible - Near Infrared Spectra With Chemometrics

Vis/NIR reflectance spectroscopy for non-destructive diagnosis of Fusarium spp. infection in postharvest potato tubers (Solanum tuberosum)

Determination of the Betacyanin and Betaxanthin Contents of Red Beet (Beta Vulgaris) Powder Using Partial Least Square Regression Based on Visible-Near Infrared Spectra

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