近红外高光谱成像技术预测SO2预处理和脱水芒果的品质。

IF 2.6 3区 农林科学 Q2 FOOD SCIENCE & TECHNOLOGY
Wayan Dipasasri Aozora, Achiraya Tantinantrakun, Anthony Keith Thompson, Sontisuk Teerachaichayut
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引用次数: 0

摘要

研究了一种无损预测SO2预处理和脱水芒果品质指标的方法。利用近红外高光谱成像技术(NIR-HSI)对芒果的总可溶性固形物(TSS)和二氧化硫(SO2)含量进行了分类和预测。利用实验室近红外高光谱成像系统在935 ~ 1720 nm波长范围内对样品进行扫描,获取光谱数据。利用偏最小二乘回归(PLSR)对光谱数据(自变量)建立质量指标(因变量)的标定模型。采用Savitzky- Golay平滑预处理建立TSS的校准模型,采用原始光谱建立SO2含量的校准模型。模型对TSS和SO2含量的预测相关系数(Rp)分别为0.82和0.83,预测均方根误差(RMSEP)分别为2.42%和56.40 mg/kg。利用该模型对每幅SO2预处理和脱水后的芒果图像进行HSI扫描,预测每个像素的TSS和SO2含量,并插值到线性颜色尺度,得到预测图像,通过颜色可视化显示SO2预处理和脱水后的芒果的TSS和SO2含量。结果表明,NIR-HSI可用于预测TSS和SO2含量。这些因素是SO2预处理和脱水芒果的重要品质指标。图片摘要:补充资料:在线版本包含补充资料,网址为10.1007/s13197-024-06132-8。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Near-infrared hyperspectral imaging for predicting the quality of SO2 pre-treated and dehydrated mango.

This study tested a non-destructive technique for predicting quality indices of SO2 pre-treated and dehydrated mangoes. Near-infrared hyperspectral imaging (NIR-HSI), a non-destructive technique, was tested for classifying and predicting total soluble solids (TSS) and sulfur dioxide (SO2) content of the treated mangoes. The samples were scanned through a laboratory-based near infrared hyperspectral imaging system within the wavelength range of 935-1720 nm in order to acquire the spectral data. The calibration models were developed for quality indices (dependent variables) by spectral data (independent variables) using partial least square regression (PLSR). Savitzky- Golay smoothing pretreatment was used for creating the calibration model for TSS while the original spectra were used for creating the calibration model for SO2 content. The models obtained predictive results for TSS and SO2 content with correlation coefficient of prediction (Rp) 0.82 and 0.83 respectively and root mean square error of prediction (RMSEP) of 2.42% and 56.40 mg/kg, respectively. The models were used to predict TSS and SO2 content in each pixel of each SO2 pre-treated and dehydrated mango image using HSI scanning and interpolated to a linear-color-scale in order to obtain the predictive images, which showed TSS and SO2 content of SO2 pre-treated and dehydrated mango by color visualization. The results showed that NIR-HSI could possibly be used to predict TSS and SO2 content. These factors are important quality indices of SO2 pre-treated and dehydrated mangoes.

Graphical abstract:

Supplementary information: The online version contains supplementary material available at 10.1007/s13197-024-06132-8.

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来源期刊
CiteScore
7.70
自引率
0.00%
发文量
274
审稿时长
11 months
期刊介绍: The Journal of Food Science and Technology (JFST) is the official publication of the Association of Food Scientists and Technologists of India (AFSTI). This monthly publishes peer-reviewed research papers and reviews in all branches of science, technology, packaging and engineering of foods and food products. Special emphasis is given to fundamental and applied research findings that have potential for enhancing product quality, extend shelf life of fresh and processed food products and improve process efficiency. Critical reviews on new perspectives in food handling and processing, innovative and emerging technologies and trends and future research in food products and food industry byproducts are also welcome. The journal also publishes book reviews relevant to all aspects of food science, technology and engineering.
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