Quantifying soluble sugar in super sweet corn using near-infrared spectroscopy combined with chemometrics

Yang, Quannu; Yang, Xinhao; Zhang, Qianling; Wang, Yunbo; Han, Song; Huang, Furong

doi:10.1016/j.ijleo.2020.165128

Cited by 15 publications

(5 citation statements)

References 19 publications

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“…(Abeysiri et al, 2013). This shows the richness of this species in natural products and turned out to have biological activities and a concentration of 5.51 ± 0.04 mg/g DM of soluble sugars close to that of the study Quannu (Yang et al, 2020). The content of soluble sugars is a key factor affecting the quality of plants.…”

Section: Assay By Spectrophotometer Concentrationsupporting

confidence: 73%

Phytochemical Screening and Evaluation of the Antibacterial Activity of Galactites Tomentosa Extract

Boudjema¹,

Azeddine²,

Ikram³

et al. 2022

PLANT ARCHIVES

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The aim of current study was to investigate a wild plant, known by purple milk thistle (Galactites tomentosa), very abundant in El-Kala National Park and the Mediterranean basin. This study was designed to evaluate the antibacterial activity effects of the Galactites tomentosa. The extract was further analyzed by spectrophotometry, to measure the sugars (5,51 ± 0,04 mg/g DM), the proteins (14,86 ± 0,30 g/100g DM), polyphenols (12,24 ± 0,10 GAE mg/g DM), and chlorophyll pigments (1,13 ± 0,20 mg/g FM). This work is considered as an introduction to the valuation of El-Kala National Park (EKNP) plants, for the development of the food and pharmaceutical industry.

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Section: Assay By Spectrophotometer Concentrationsupporting

confidence: 73%

Phytochemical Screening and Evaluation of the Antibacterial Activity of Galactites Tomentosa Extract

Boudjema¹,

Azeddine²,

Ikram³

et al. 2022

PLANT ARCHIVES

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“…The results indicated that the optimal prediction model combined the BS preprocessing method with the rep variable selection method, achieving R 2 and RMSE values of 0.59 and 1.02, respectively. These values were lower than the R 2 and RMSE values obtained when predicting the soluble sugar content of sweet maize (R 2 = 0.8431, RMSE = 5.8292) (Yang et al, 2020). The discrepancy may be attributed to the freshness of the chestnut used for spectral data collection, their high water content, and the complex texture and composition of the chestnut kernels.…”

Section: Discussionmentioning

confidence: 61%

“…With the advancements in chemometric methods and spectroscopic instrument hardware technology, spectroscopic analysis has become a mainstream technique for non-destructive detection of the internal quality of fruits (Maria et al, 2021). Hyperspectral imaging technology is a rapid, eco-friendly, nondestructive, and efficient detection method (Yu et al, 2009;Yang et al, 2020). It reflects the absorption information of molecules, such as hydrogen-containing groups like C-H, O-H, and N-H, in the ensemble and multiplicity frequencies.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging-based prediction of soluble sugar content in Chinese chestnuts

Yang

Gong

Jiang

2023

Front. For. Glob. Change

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Soluble sugars are critical determinants of fruit quality and play a significant role in human nutrition. Chestnuts, rich in soluble sugars, derive their sweetness from them. However, their content varies with cultivar, location, and environmental conditions. Traditional methods for determining soluble sugar content in chestnuts are time-consuming, laborious, and destructive. Therefore, there is a pressing need for rapid, non-destructive, and straightforward methods for determining soluble sugars in chestnuts to expedite genetic selection. This study aimed to develop a hyperspectral imaging-based prediction model for soluble sugar content in Chinese chestnuts. Firstly, abnormal samples were eliminated using ensemble partial least squares for outlier detection. We then compared the average original and block scale (BS) spectra, with the latter demonstrating significant differences. The BS pretreatment exhibited two small absorption peaks in the 403.7 ∼ 429.1 nm band and 454.7 ∼ 500 nm band, less fluctuation in the spectral curves from 503.2 to 687.2 nm, and a substantial increase in spectral absorption between 690.6 and 927.8 nm. Subsequently, we developed a partial least squares (PLS) model using BS pretreatment and regularized elimination (rep) variable selection, which showed better accuracy in predicting chestnut soluble sugar content than other variable selection methods. The model fitting accuracy after the spectra treatment was marginally better than that of the original spectra, with a calibration set correlation coefficient (R2) of 0.59 and root mean square error (RMSE) of 1.02, and a validation set R2 of 0.66 and RMSE of 0.94. The wavelengths at 464.3, 503.2, 539.3, 579, and 711.3 nm were identified as critical for developing the soluble sugar content prediction model. The study demonstrated the potential of Near-Infrared Spectroscopy (NIS) as a rapid and non-destructive method for predicting chestnut soluble sugar content, which could be beneficial for quality control and sorting in the food industry.

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“…The best prediction was obtained when the 1349−1513, 1842−2005, 2005−2168, and 2337−2500 nm wavelength ranges were applied. To improve the predictive accuracy, the authors performed (CARS)-Si-PLS, hence the RMSEP amounted to 5.8292 mg/g and the correlation coefficient of the prediction set was equal to 0.8431 [ 129 ].…”

Section: Near Infrared (Nir) Spectroscopy: Historical Background Amentioning

confidence: 99%

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue—Critical Overview

Aouadi

Zaukuu

Vitális

et al. 2020

Sensors

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Amid today’s stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

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Quantifying soluble sugar in super sweet corn using near-infrared spectroscopy combined with chemometrics

Cited by 15 publications

References 19 publications

Phytochemical Screening and Evaluation of the Antibacterial Activity of Galactites Tomentosa Extract

Phytochemical Screening and Evaluation of the Antibacterial Activity of Galactites Tomentosa Extract

Hyperspectral imaging-based prediction of soluble sugar content in Chinese chestnuts

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue—Critical Overview

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