Near-infrared (NIR) spectrometric technique for nondestructive determination of soluble solids content in processing tomatoes

• Published in: Journal of the American Society for Horticultural Science Vol. 123; no. 6; pp. 1089 - 1093
• Main Authors: Peiris, K.H.S, Dull, G.G, Leffler, R.G, Kays, S.J
• Format: Journal Article
• Language: English
• Published: 01.11.1998
• Subjects: BRIX; CHEMICAL COMPOSITION; COMMINUTION; COMPOSICION QUIMICA; COMPOSITION CHIMIQUE; CRUSHING; DESMENUZAMIENTO; ENSAYO; ESPECTROMETRIA; food processing; FOOD TECHNOLOGY; FORECASTING; FRAGMENTATION; nondestructive methods; NONDESTRUCTIVE TESTING; prediction; SPECTROMETRIE; SPECTROMETRY; spectroscopy; TECHNIQUE DE PREVISION; TECHNOLOGIE ALIMENTAIRE; TECNICAS DE PREDICCION; TECNOLOGIA DE LOS ALIMENTOS; TESTAGE; TESTING; TOMATE; TOMATOES
• DOI: 10.21273/jashs.123.6.1089
• ISSN: 0003-1062

A nondestructive method for measuring the soluble solids content (SSC) of individual processing tomatoes (Lycopersicon esculentum Mill.) was developed using NIR spectrometry. A diode array fiber optic spectrometer was used to measure NIR transmittance. Each fruit was scanned at two locations on opposite sides midway along the proximal-distal axis. After scanning, each fruit was processed and pureed, and SSC was determined using a refractometer. Multiple linear regression (MLR), partial least squares (PLS) regression, and neural network (NN) calibration models were developed using the second derivatives of averaged spectra from 780 to 980 nm. The validation results showed that NN calibration was better than MLR or PLS calibrations. The NN calibration could estimate the processed SSC of individual unprocessed tomatoes with a standard error of prediction of 0.52% and could classify 72% of fruit in an independent population within +/- 0.5% of SSC