Models for the rapid assessment of water and oil content in olive pomace by near‐infrared spectrometry. (12th March 2020)

• Record Type: Journal Article
• Authors: Altieri, Giuseppe; Matera, Attilio; Genovese, Francesco; Di Renzo, Giovanni Carlo
• Is Part Of: Journal of the science of food and agriculture. Volume 100:Number 7(2020)
• Page Start: 3236
• Page End: 3245
• Publication Date: 2020-03-12
• Subjects: decanter centrifuge -- extra‐virgin olive oil -- near‐infrared -- regression model -- olive pomace; Food -- Periodicals; Agriculture -- Periodicals; 664
• DOI: 10.1002/jsfa.10361
• Languages: English
• ISSNs: 0022-5142

Abstract

Abstract: BACKGROUND: The measurement of the water and oil content in olive pomace is crucial for controlling the olive‐oil extraction process. The use of near‐infrared (NIR) spectra could allow the measurement of the oil and water content in olive pomace. RESULTS: Partial least squares for pomace oil content on a dry basis reached an error of 2.5% (±0.5). Principal component regression for pomace oil content on a wet basis reached an error of 3.7% (±0.5). Both were suitable for quantitative analysis. Principal component regression for pomace water content reached an error of 6.0% (±2.3), suitable for process control. The relationship between 'ratio of standard deviation of calibration data to standard error of prediction data' and 'range of confident prediction error percentage' was investigated, it results of hyperbolic type, the constant of the hyperbolic equation depends on the product under analysis: for the olive pomace this constant is equal to 45.60 (±1.78). CONCLUSION: Near‐infrared analysis confirmed the possibility of determining the oil and water content in the olive pomace, which is important in the olive oil extraction process control. A new algorithm was used, together with standard statistical algorithms, to identify and remove the less useful wavelengths from the model, improving the overall prediction performance. A new parameter (the 'range of confident prediction error percentage') has been proposed for estimating the model's prediction error in an objective way. © 2020 Society of Chemical Industry