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首页> 外文期刊>Food analytical methods >Texture prediction in Intact green asparagus by near infrared (NIR) spectroscopy, assaying linear and non-linear regression strategies.
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Texture prediction in Intact green asparagus by near infrared (NIR) spectroscopy, assaying linear and non-linear regression strategies.

机译:通过近红外(NIR)光谱分析完整的绿芦笋中的纹理,预测线性和非线性回归策略。

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摘要

Texture-related parameters were assessed in intact green asparagus at harvest and during postharvest storage using near-infrared spectroscopy combined with MPLS and LOCAL algorithms. Three spectrophotometers were evaluated for this purpose: a monochromator (range, 400-2,500 nm), a diode-array Vis-NIR spectrophotometer (range, 400-1,700 nm), and a handheld micro-electro-mechanical system (MEMS) spectrophotometer (range, 1,600-2,400 nm). Three hundred green asparagus spears (cv. "Grande") were used to obtain calibration models based on reference data and NIR data. Results for maximum shear force showed that LOCAL algorithm improved the predictive capacity of models constructed using all three NIRS instruments, increasing r 2 by 24, 16, and 56 % and reducing the SEP(c) values by 11, 8, and 14 %, respectively. For cutting energy, the LOCAL also improved the predictive capacity of the models (r 2 increased by 3 % for the monochromator and the diode-array instrument and by 6 % for the MEMS device; and the SEP(c) decreased by 3 % in the three instruments). It is worth noting that while the monochromator and diode-array instruments displayed similar predictive capacity for the parameters tested, the MEMS instrument achieved slightly poorer results but has clear advantages for the measurement of texture in intact asparagus, being economical, portable, and easy to use in situ. copyright Springer Science+Business Media New York 2013.
机译:使用近红外光谱结合MPLS和LOCAL算法,在收获时和收获后的储存过程中,在完整的绿芦笋中评估了与纹理相关的参数。为此,对三种分光光度计进行了评估:单色仪(范围400-2,500 nm),二极管阵列Vis-NIR分光光度计(范围400-1,700 nm)和手持式微机电系统(MEMS)分光光度计(范围1,600-2,400 nm)。使用三百支绿芦笋矛(cv。“ Grande”)基于参考数据和NIR数据获得校准模型。最大剪切力的结果表明,LOCAL算法提高了使用所有三种NIRS仪器构建的模型的预测能力,将r 2 分别提高了24%,16%和56%,并将SEP(c)值降低了11% ,8%和14%。为了降低能耗,LOCAL还提高了模型的预测能力(对于单色仪和二极管阵列仪器,r 2 增加了3%,​​对于MEMS设备,增加了6%; SEP( c)在三种工具中下降了3%。值得注意的是,虽然单色仪和二极管阵列仪器对测试的参数显示出相似的预测能力,但MEMS仪器的结果稍差,但在测量完整芦笋的质地方面具有明显的优势,经济,便携且易于操作原位使用。版权所有Springer Science + Business Media纽约,2013年。

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