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

Abstract

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.