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Data processing approaches and strategies for non-destructive fruits quality inspection and authentication: a review

机译:非破坏性水果质量检验和认证的数据处理方法和策略:综述

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Fruit quality inspection and authentication instruments are the essential requirement at the different stages of fruit processing industries from harvesting to market. In recent years, various intelligent analytical methods such as electronic nose, gas chromatography and mass spectroscopy, UV-Vis-NIR spectroscopy, machine vision, hyperspectral imaging and many more have been evolved to access the fruit quality at different stages such as maturity judgement of an on-tree fruit, shelf life measurement of harvested fruit, other quality parameters measurement of various fruit products at processing industries etc. Information extracted from various analytical methods needs to be processed using different data processing approaches and strategies, which plays the major role to bring the intelligence in the analytical instruments. Although, highly promising results have been reported to process data acquired from similar type of sensory panel (gas sensor array in electronic nose) and single sensing technique (impedance measurement) but still there are several challenges to process data acquired from multiple sensing techniques fusion (similar or complementary in nature) to predict better informative results. Recently, there is a growing interest in the direction of multiple sensing techniques fusion to extract better information from fruit samples in a reliable manner and also in less time. This paper presents an extensive review of classical and modern data processing approaches and strategies that have been used for single and multiple non-destructive sensing methods in the area of fruit quality inspection and authentication. Various approaches and strategies for preprocessing, data fusion, feature extraction, model design, multi-modal data processing, training, testing and validation for single and multiple sensing techniques have been briefly explained in the presented review. The presented review also discusses the need, scope, and challenges of data processing methods for multiple sensing techniques fusion. Different commercially available handheld and lab level analytical instruments also have been reviewed based on their intelligence, complexity and quality parameters prediction.
机译:水果质量检验和认证仪器是水果加工行业采集到市场的不同阶段的基本要求。近年来,各种智能分析方法,如电子鼻,气相色谱和质谱,UV-Vis-Nir光谱,机器视觉,高光谱成像等,以进入不同阶段的果实质量,如成熟度判断树木果实,包装寿命测量的收获果实,其他品质参数测量各种水果产品的处理行业等。从各种分析方法中提取的信息需要使用不同的数据处理方法和策略来处理,这起到了主要作用在分析仪器中带来智能。虽然已经报道了高承诺的结果来处理从类似类型的感官面板(电子鼻中的气体传感器阵列)获取的数据和单一传感技术(阻抗测量),但仍然有几个挑战来处理从多种传感技术融合获取的数据(在自然界中类似或互补)预测更好的信息结果。最近,对多种感测技术融合方向的兴趣日益增长,以以可靠的方式提取来自水果样品的更好信息,并且在较少的时间内也是如此。本文提出了对古典和现代数据处理方法和策略的广泛审查,这些方法已经用于果实质量检验和认证领域的单一和多种无损检测方法。在审查中,已经简要介绍了针对单一和多种传感技术的各种方法和策略,数据融合,特征提取,模型设计,多模态数据处理,培训,测试和验证,并在审查中解释。本综述还讨论了多种传感技术融合的数据处理方法的需求,范围和挑战。根据其智能,复杂性和质量参数预测,还已经过审查了不同的商用手持式手持和实验室层面分析仪器。

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