Models to improve the non‐destructive analysis of persimmon fruit properties by VIS/NIR spectrometry. (13th June 2017)

• Record Type: Journal Article
• Authors: Altieri, Giuseppe; Genovese, Francesco; Tauriello, Antonella; Di Renzo, Giovanni Carlo
• Is Part Of: Journal of the science of food and agriculture. Volume 97:Number 15(2017)
• Page Start: 5302
• Page End: 5310
• Publication Date: 2017-06-13
• Subjects: regression -- RPD -- PLS -- PCR -- SVM -- ensemble trees; Food -- Periodicals; Agriculture -- Periodicals; 664
• DOI: 10.1002/jsfa.8416
• Languages: English
• ISSNs: 0022-5142

Abstract

Abstract: BACKGROUND: Visible–near‐infrared spectrometry is a technique suitable for assessing chemical and physiological properties of fruit. Some models of calibration/prediction have been tested in order to assess the feasibility of a visible–near‐infrared sensor in order to monitor persimmon fruit colour, firmness, soluble solids, titratable acidity and soluble tannins. RESULTS: Five regression models were investigated: principal component, partial least squares, stepwise, support vector machines and ensembles of trees. These models were assessed by a 10‐fold cross‐validation with a new strategy for both outlier removal and wavelength reduction; furthermore, their statistical significance was evaluated by 100 Monte Carlo simulation runs. Principal component regression allowed us to build excellent and/or very good fit/prediction models. The results (in terms of RPD as standard deviation to performance standard error ratio) are: 9.23 (±0.26) for colour index, 10.18 (±0.37) for firmness, 7.15 (±0.28) for soluble solids content, 7.87 (±0.31) for titratable acidity and 8.91 (±0.33) for soluble tannins content. CONCLUSION: The proposed strategy, for outlier removal and wavelength reduction, allowed the achievement of useful results. Principal component regression fit/prediction capability produced excellent results. Conversely, partial least squares regression showed fair/poor results and the remaining tested models performed badly on real data. © 2017 Society of Chemical Industry