Prediction of quality parameters of food residues using NIR spectroscopy and PLS models based on proximate analysis

• Main Authors: Magale Karine Diel RAMBO, Márcia Miguel Castro FERREIRA, Polyana Morais de MELO, Claúdio Carneiro SANTANA JUNIOR, Daniel Assumpção BERTUOL, Michele Cristiane Diel RAMBO
• Format: Article
• Language: English
• Published: Sociedade Brasileira de Ciência e Tecnologia de Alimentos
• Series: Food Science and Technology
• Subjects: biomass; figures of merit; pre-treatments; external validation
• Online Access: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612020000200444&lng=en&tlng=en
• ISSN: 0101-2061; 1678-457X

Abstract The real-time prediction in biorefinery industries has become essential. Models using partial least square regression (PLS) were developed to predict moisture, ash, volatile matter, fixed carbon and organic matter of coconut and coffee residues. In this study, 49 samples were collected and near infrared spectroscopy were applied to predict moisture, ash, volatile matter, fixed carbon and organic matter. For external validation 25% of the set samples were used. Moisture and volatile matter were predicted with coefficients of determination (R2cal) above 0.90, and standard errors (RSD) of the estimate of 14.4% and 2.26%, respectively. Models of ash and organic matter show R2cal > 0.77 and RSD values < 20.4%. For the external validation, the low deviations show the approximation between reference and predicted values and good prediction with R2pred > 0.70. All calibration models were acceptable for sample screening. This study demonstrates that PLS can be used to predict biomass composition of different species, with very low costs and time.