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A review on the applications of Near-Infrared spectrometer and Chemometrics for the agro-food processing industries

机译:近红外光谱仪和化学计量学在农业食品加工业中的应用综述

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The purpose of this review article is to outline the recent progress in Near Infrared (NIR) spectroscopy and spectral data analysis using Chemometrics in agro-food processing industries. In recent years, all the processing industries have created the need for cost effective and non-destructive quality control analysis systems to improve their products significantly. Among varies process industries, the agro-food processing industries encompasses all steps from the grower to the customer, involves substantially different types of process, which require monitoring for safety, conformance to specification and profit optimization. The agro-food processing industries cover a wide range of activities utilizing agriculture farm, animal and forestry based products as a raw materials. There are certain traditional quality inspection systems in agro based industries such as agricultural green houses, product stock waste, animal feed, farm machinery, fertilizer, flower bulbs, seeds & seedlings, fresh vegetables, fruits, grains, nuts & kernels, oil seeds and plant & animal oil etc. to assess the quality of their food products by employing machine vision technology to identify the external defects of the products and X-Ray based imaging to identify internal defects of the products, but the NIR spectroscopy technique can also be applied for the above said industries, because it is particularly powerful in non-invasive, non-destructive, speed in analysis, flexibility in adapting to different sample states. However, this NIR spectroscopy requires a unique way to extract relevant physical and chemical information from the sample's spectral data and this can be performed by only a new statistical approach namely Chemometric algorithms. In this review, the principles and advantages of NIR spectroscopy are described first and then its application to various organic products in agro-food processing industries.
机译:本文的目的是概述在农业食品加工业中使用化学计量学的近红外(NIR)光谱学和光谱数据分析的最新进展。近年来,所有加工业都对成本有效且无损的质量控制分析系统提出了更高的要求,以提高其产品质量。在各种加工行业中,农业食品加工行业涵盖了从种植者到客户的所有步骤,涉及实质上不同的过程类型,这些过程需要进行安全性监控,符合规格要求和利润优化。农业食品加工业利用农业,畜牧和林业产品为原料,涵盖了广泛的活动。在农业产业中有某些传统的质量检查系统,例如农业温室,产品库存废物,动物饲料,农业机械,肥料,鳞茎,种子和幼苗,新鲜蔬菜,水果,谷物,坚果和果仁,油料和动植物油等通过使用机器视觉技术来识别产品的外部缺陷并通过基于X射线的成像来识别产品的内部缺陷来评估其食品的质量,但是也可以应用近红外光谱技术对于上述行业,因为它在非侵入性,非破坏性,分析速度方面非常强大,并且在适应不同样品状态方面具有灵活性。但是,这种近红外光谱需要一种独特的方法来从样品的光谱数据中提取相关的物理和化学信息,而这只能通过一种新的统计方法即化学计量学算法来执行。在这篇综述中,首先描述了近红外光谱的原理和优点,然后将其应用于农业食品加工业中的各种有机产品。

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