化学计量学方法在果蔬领域的应用

Application of chemometrics methods in fruit and vegetable field

中文关键词:  果蔬  主成分分析  偏最小二乘  人工神经网络

英文关键词:fruits and vegetables  principal component analysis  partial least squares  artificial neural network

中文摘要:

      果蔬富含维生素、矿物质和膳食纤维等营养物质, 是人们补充营养必不可少的食品之一。但果蔬在加工储存过程中存在农药残留、营养成分损失、品质下降等一系列问题, 如何保证果蔬的食用安全和食用品质一直是果蔬研究领域的焦点。测定果蔬的营养理化指标是评价果蔬品质的有效手段, 然而过多的指标通常会产生大量繁杂冗余的数据, 需要专门的工具对数据进行分析处理。化学计量学方法是进行数据处理的有效手段, 近年来在果蔬领域已得到广泛应用。本文综述了主成分分析、偏最小二乘和人工神经网络等化学计量学方法在果蔬品质差异评价、果蔬农药残留检测、果蔬理化分析、果蔬保鲜效果优劣及果蔬生产工艺优化等方面的应用, 讨论了各种方法的优缺点, 为化学计量学方法在果蔬领域中的应用提供参考。

英文摘要:

      Fruits and vegetables are one of the essential foods for people to enhance nutrition, which are rich in nutrients such as vitamins, minerals, and dietary fiber. However, there were a series of safety issues in the processing and storage of fruits and vegetables, such as pesticide residue, loss of nutritional components, quality decline. How to ensure the food safety and quality of fruits and vegetables had always been the focus of research in the field of fruits and vegetables. The nutritional and physicochemical indicators of fruits and vegetables were means to evaluate their quality. But a amount of complex and redundant data was generated through too many indicators, which required specialized tools to analyze and process. Chemometrics is an effective method for data processing. In recent years, it has been used in the field of fruits and vegetables. This paper summarized the applications of principal component analysis, partial least squares, and artificial neural networks in the evaluation of fruit and vegetable quality differences, pesticide residue detection, physicochemical analysis, preservation effect, and production process optimization, and discussed the advantages and disadvantages of various methods, which provides references for the application of chemometrics methods in the field of fruits and vegetables.