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Comparison of hand-held near infrared spectrophotometers for fruit dry matter assessment

机译:果实干物质评估的手持近红外分光光度计的比较

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摘要

Comparisons are reported for developing predictive models for dry matter across a wide variety of fruits with near infrared spectroscopy instrumentation, using a number of commercially available hand-held portable instruments (NIRVANA by Integrated Spectronics, F-750 by Felix Instruments, H-100C by Sunforest and SCiO by Consumer Physics) and an in-house laboratory based instrument (Benchtop). Three intrinsic (same fruit type) and combined (all fruit types) data sets were created from two separate batches of fruit populations. The first batch (Lot I) consisted of 205 ripe fruits from three different main fruit types (apples, kiwifruit and summerfruit) and 12 distinct fruit sub-categories. The second batch (Lot II) consisted of 91 ripe fruits from two different fruit types (apples and kiwifruit) and seven distinct fruit sub-categories. The laboratory based Benchtop instrument performed the best overall with typically higher prediction r(2) values (> 0.92). The hand-held instruments delivered moderate to high r(2) values between 0.8 and 0.95. Results obtained with the intrinsic data sets revealed typically lower root mean square errors of prediction for apples and kiwifruit (0.32% to 0.73%) and larger prediction errors for summerfruit (0.53% to 0.82%). Some large performance variations between instruments of the same type were observed suggesting caution in evaluating the relative performance of different instrument types or formats on the basis of data generated with just a single instrument and/or data set. However, performance differences between the different hand-held portable instruments, on the same data sets, were often not statistically significant (p< 0.05). Instrument choice for any particular application will likely come down to matters not considered here, such as, for example, ease and accuracy during in-field operation and overall reliability.
机译:据报道,使用多种商用的手持便携式仪器(Nirvana,F-750通过Felix Instruments,H-100C,H-100C,H-100C,H-100C,H-100C,H-100C的集成光谱,H-100C,H-100C,H-100C,H-100C,H-100C,H-100C,H-100C的集成光谱,H-100C,H-100C,H-100C,H-100C,H-100C的F-750消费物理学的自助和Scio)和基于内部实验室的仪器(Benchtop)。三种内在(相同的水果类型)和组合(所有果实类型)数据集是由两种单独的果实种群创建的。第一个批量(批次I)由来自三种不同的主要果实类型(苹果,猕猴桃和夏季果馆)和12种不同的水果子类别组成的205份成熟水果。第二批(Lot II)由来自两种不同的水果(苹果和猕猴桃)和七种不同的水果子类别的91种成熟果实组成。基于实验室的台式仪器具有通常更高的预测R(2)值(> 0.92)的最佳总体。手持式仪器在0.8和0.95之间的温和仪表中递送至高r(2)值。具有内在数据集获得的结果揭示了苹果和猕猴桃的预测的较低的均方根误差(0.32%至0.73%),夏季呋氏的预测误差(0.53%至0.82%)。观察到相同类型的仪器之间的一些大的性能变化,旨在小心根据仅使用单个仪器和/或数据集生成的数据来评估不同仪器类型或格式的相对性能。然而,在同一数据集上不同的手持便携式仪器之间的性能差异通常在统计学上没有统计学意义(P <0.05)。任何特定应用的仪器选择可能会归结为这里不考虑的事项,例如在现场操作期间的缓解和准确性和整体可靠性。

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