Sorting of Fruit Using near Infrared Spectroscopy: Application to a Range of Fruit and Vegetables for Soluble Solids and Dry Matter Content

By Kerry B. Walsh, M. Golić, C. Greensill. 2004

146 citations recorded by the source corpus.

The performance of a single instrumentation platform, incorporating the use of a tungsten halogen light source, body transmittance optics and a silicon photodiode array detector, and a uniform chemometric approach is reported for the application of assessment of determination of soluble solids and dry matter content of a range of fruit. Spectra were acquired at integration times of 30 ms or less, with integration time varied between fruit types to achieve a similar signal level. Calibration performance was compared in terms of root mean standard error of cross validation (RMSECV), regression coefficient (R), and the SDR (SDR = SD / RMSECV (SD is standard deviation)]. The technology was well suited to sorting on soluble solids content (SSC) in apple (RMSECV 0.22%, SDR > 5; R 0.98), and useful, in decreasing order of accuracy, for sorting of stonefruit, mandarin, banana, melons, onions, tomato and papaya (RMSECV 1.1%, SDR 1.6, R 0.79). The technology also performed well in sorting on dry matter content in kiwifruit (RMSECV 0.38%, SDR > 3, R 0.95), and useful, in decreasing order of accuracy, for sorting of banana, mango, avocado, tomato and potato (RMSECV 1.0%, SDR 1.7, R 0.79). The limitations of the application of the technology to fruit sorting is discussed in terms of fruit type (“skin” thickness) and population range. For example, calibration RMSECV was only 0.20% on tomato SSC, but as population variation was low (SD 0.30%), a poor R (0.77) and SDR (1.5) was obtained.

Subfields: Analytical Chemistry, Biomedical Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/1c2ba46c0eeb195ff3610fd6f15422d6fb1ce61f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

In Biomedical Engineering

Kerry Brian Walsh

In Industrial and Manufacturing Engineering

Citations in the corpus

Non-destructive evaluation of maturity and quality parameters of pomegranate fruit by visible/near infrared spectroscopy (2017, methodology): “Gomez et al.[26] obtained better results (r = 0.94 and RMSEP = 0.35 °Brix) when they used their model for mandarins.”
Non-invasive techniques for measurement of fresh fruit firmness. (2009, background): “TheSWNIRS is accepted for theassessmentof total soluble solids (TSS) and drymatter (DM) in intact fresh fruit, in both an academic (Dull et al., 1989; Kawano et al., 1992; Slaughter, 1995; Guthrie and Walsh, 1997; Walsh et al., 2004) and a commercial sense.”
Assessment of internal quality attributes of mandarin fruit. 1. NIR calibration model development (2005, methodology): “Introduction Near infrared spectroscopy (NIRS) has been applied to the sorting of intact fruit on total soluble solids (TSS) and dry matter (DM) content (Walsh et al. 2004), with commercial application to pack-house fruit sorting lines for the sorting of sweetness of citrus, apples, pears, and peach”