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

Author:

Kaur Harpreet123,Künnemeyer Rainer13,McGlone Andrew2

Affiliation:

1. School of Engineering, The University of Waikato, Hamilton, New Zealand

2. The New Zealand Institute for Plant & Food Research Limited, Ruakura Research Centre, Hamilton, New Zealand

3. The Dodd Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand

Abstract

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 r2 values (>0.92). The hand-held instruments delivered moderate to high r2 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.

Publisher

SAGE Publications

Subject

Spectroscopy

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