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Study on Inspection System of Quality for Fruit and Vegetables using Spectral Imaging(UV-NIR).

光谱成像（UV-NIR）果蔬品质检测系统研究。

基本信息

批准号：
15380175
负责人：
NAGATA Masateru
金额：
$ 5.63万
依托单位：
University of Miyazaki
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2005
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-15380175/
关键词：
Hyperspectral Imaging Spectral Image Strawberry Quality Estimation Soluble Solids Content Firmness Near Infrared spectral method Non-destroying measurement 品質判定品質検査システム青果物 NIR VIS 分光波長

项目摘要

Consumers are demanding for higher quality and better safety of agricultural produce. While external food quality inspection is now more commonly done during post-harvest processing, the non-destructive measurement of internal quality such as sugar content and firmness is becoming more important. Thus, more accurate quality measurement techniques are needed. The main goal of this research is to develop prediction models that can estimate soluble solids content (SSC) and firmness in strawberries and SSC in tomatoes using hyperspectral imaging in visible (VIS) and near-infrared (NIR).1.Strawberries1)A VIS liquid crystal tunable filter-based hyperspectral imaging that took images from 450 nm to 650 nm at 2-nm intervals was used to relate spectral reflectance data with firmness. In the technically ripe sample sets, the five-predictor firmness model (510, 650, 644, 628, and 598 nm) had an SEP of 0.364 and a correlation coefficient r of 0.784.2)Similarly, using NIR hyperspectral images (650-1000nm at 5-nm intervals) were taken to develop calibration models for firmness and SSC using stepwise multiple linear regression. The three-wavelength prediction model for firmness had a correlation of 0.786 and SEP of 0.350 (50% to Full-ripe group). While for SSC, the five-wavelength prediction model yielded a correlation of 0.870 and SEP of 0.530 (70% to Full-ripe group).2.TomatoesUsing NIR hyperspectral images from 650 nm to 1100 nm at 10-nm intervals calibration models were developed to relate the second derivative of the absorbance spectral data to the fruit's SSC. The five-wavelength model had a correlation coefficient r of 0.939 and SEC of 0.094 for the Full-ripe samples.Hyperspectral imaging was found very useful for the non-destructive estimation of internal quality of fruits and fruit-vegetables.Papers based on this research were chosen to receive the IET Select Paper Award during the international meetings of ASAE in 2004 and 2005.

消费者对农产品质量和安全性的要求越来越高。虽然外部食品质量检测现在更常见于收获后加工过程中，但对内部质量（如糖含量和硬度）的非破坏性测量变得越来越重要。因此，需要更精确的质量测量技术。本研究的主要目标是开发预测模型，该模型可以使用可见光（维斯）和近红外（NIR）中的高光谱成像来估计草莓中的可溶性固形物含量（SSC）和硬度以及番茄中的SSC。1.草莓1）基于维斯液晶可调谐滤波器的高光谱成像，其在2- 30 nm处拍摄450 nm至650 nm的图像。nm间隔用于将光谱反射率数据与硬度相关联。在技术成熟的样品组中，五个预测值硬度模型（510，650，644，628和598 nm）的SEP为0.364，相关系数r为0.784。2）类似地，使用NIR高光谱图像（650- 1000 nm，间隔5 nm），使用逐步多元线性回归建立硬度和SSC的校准模型。三波长预测模型的相关系数为0.786，SEP为0.350（50%对全熟组）。而对于可溶性固形物含量，五波长预测模型的相关系数为0.870，预测误差为0.530（70%）。2.番茄利用650 ~ 1100 nm、间隔10 nm的近红外高光谱图像，建立了吸收光谱数据二阶导数与可溶性固形物含量的校正模型。五波长模型的相关系数r为0.939，SEC为0.094。高光谱成像被认为是非常有用的水果和水果蔬菜的内部品质的无损评估。基于这项研究的论文被选为2004年和2005年ASAE国际会议期间的IET选择论文奖。