Non-destructive determination of water content in fruits using Vis-NIR spectroscopy

Food Research Pub Date : 2024-03-30 DOI:10.26656/fr.2017.8(s2).14

H. Z. Amanah, E. Pratiwi, D. N. Rahmi, M. Pahlawan, R. Masithoh

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Abstract

Visible near-infrared (Vis-NIR) spectroscopy is less accurate for detecting -OH molecules, therefore, the use of Vis-NIR spectroscopy is challenging for samples containing high water content, such as fruits. However, as Vis-NIR spectroscopy is a portable and economical instrument, it can be used in the field by small-scale farmers. This study aimed to evaluate Vis-NIR spectra to measure water content in fruits. Four fruits were used in this study, including dragon fruit, guava, sapodilla and banana (100 pieces each). All fruits were randomly divided into a calibration set (two-thirds of the samples) and a prediction set (one-third of the samples). Water content was predicted using partial least square regression (PLSR) analysis. The PLSR calibration model had a coefficient of determination (R²) of 0.29 for dragon fruit, 0.63 for guava, 0.62 for sapodilla and 0.80 for banana. The prediction model had an R² of 0.11 for dragon fruit, 0.63 for guava, 0.52 for sapodilla and 0.75 for banana. These results show that Vis-NIR spectroscopy has the potential to predict water content in relatively low water-content fruits, such as bananas.

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利用可见光-近红外光谱法无损测定水果中的水分含量

可见光近红外光谱（Vis-NIR）检测-OH 分子的准确性较低，因此，对于水果等含水量较高的样品来说，使用可见光近红外光谱具有挑战性。然而，由于可见光-近红外光谱仪是一种便携且经济的仪器，小规模农户可以在田间使用该仪器。本研究使用了四种水果，包括火龙果、番石榴、无患子和香蕉（各 100 件）。所有水果被随机分为校准集（三分之二的样品）和预测集（三分之一的样品）。含水量通过偏最小二乘法回归（PLSR）分析进行预测。偏最小二乘法回归校准模型的判定系数（R²）分别为：火龙果 0.29，番石榴 0.63，山榄 0.62，香蕉 0.80。火龙果、番石榴和香蕉预测模型的 R² 分别为 0.11、0.63、0.52 和 0.75。这些结果表明，可见光-近红外光谱法有可能预测香蕉等含水量相对较低的水果的含水量。

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Food Research

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