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首页> 外文期刊>Journal of near infrared spectroscopy >Visualisation of fat and fatty acid distribution in beef using a set of filters based on near infrared spectroscopy
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Visualisation of fat and fatty acid distribution in beef using a set of filters based on near infrared spectroscopy

机译：使用一组基于近红外光谱的滤光镜可视化牛肉中脂肪和脂肪酸的分布

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摘要

Food quality is strongly affected by its components and their spatial distributions. Recently, spectroscopic methods have been widely applied as a non-destructive and rapid method to measure food quality. Although it is a versatile technique, the measurement system is extremely costly for practical use. In this paper, we propose a simple measurement system using a small set of band-pass filters. A food constituent was predicted using output from the band-pass filters as input for a multiple linear regression model, and the bands were designed to obtain high prediction accuracy characterised by the determination coefficient, using hyperspectral data by the optimisation approach. We designed three sets of filters to separately determine contents such as oleic acid, total unsaturated fatty acid and fat content in raw beef using NIR hyperspectral data, and then we implemented these designs as real optical filters. By mounting the filter in front of the lens of an NIR monochrome camera, we captured a set of filtered images. We then performed a pixel-by-pixel prediction of the content to enable the spatial distribution to be visualised. The determination coefficient (R~2) and prediction error, which we characterized by the root mean square error of cross-validation (RMSECV), of this filtering method (R~2 = 0.638-0.739, RMSECV = 3.13-5.15) were superior to those obtained with partial least squares (PLS) regression using hyperspectral measurements (R~2 = 0.610-0.643, RMSECV = 3.70-6.12). Our method, therefore, facilitates the application of a hyperspectral technique for practical use.
机译：食品质量受到其组成及其空间分布的强烈影响。近来，光谱法已被广泛地用作测量食品质量的无损快速方法。尽管这是一种通用技术，但测量系统的实际使用成本非常高。在本文中，我们提出了一种使用少量带通滤波器的简单测量系统。使用带通滤波器的输出作为多元线性回归模型的输入来预测食物成分，并通过优化方法使用高光谱数据将波段设计为获得具有确定系数特征的高预测精度。我们设计了三套滤光片，以使用NIR高光谱数据分别确定生牛肉中的油酸，总不饱和脂肪酸和脂肪含量，然后将这些设计实现为实际的滤光片。通过将滤光片安装在NIR单色相机的镜头前面，我们捕获了一组滤过的图像。然后，我们对内容进行逐像素预测，以使空间分布可视化。以该方法的交叉验证均方根误差（RMSECV）为特征的确定系数（R〜2）和预测误差（R〜2 = 0.638-0.739，RMSECV = 3.13-5.15）更好与使用高光谱测量通过偏最小二乘（PLS）回归获得的结果（R〜2 = 0.610-0.643，RMSECV = 3.70-6.12）。因此，我们的方法促进了高光谱技术的实际应用。

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