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Sorting of fruit using near infrared spectroscopy : application to a range of fruit and vegetables for soluble solids and dry matter content
journal contributionposted on 2017-12-06, 00:00 authored by Kerry WalshKerry Walsh, M Golic, Colin GreensillColin Greensill
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 30ms 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.