Rapid Assessment of Anthocyanins Content of Onion Waste through Visible-Near-Short-Wave and Mid-Infrared Spectroscopy Combined with Machine Learning Techniques

Tziolas, Nikolaos; Ordoudi, Stella A.; Tavlaridis, Apostolos; Karyotis, Konstantinos; Zalidis, George; Mourtzinos, Ioannis

doi:10.3390/su13126588

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Rapid Assessment of Anthocyanins Content of Onion Waste through Visible-Near-Short-Wave and Mid-Infrared Spectroscopy Combined with Machine Learning Techniques

Nikolaos Tziolas

Stella A. Ordoudi

Apostolos Tavlaridis

³,

Konstantinos Karyotis

¹,

George Zalidis

¹ and

Ioannis Mourtzinos

^3,*

Laboratory of Remote Sensing, Spectroscopy, and GIS, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Laboratory of Food Chemistry and Technology, School of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Author to whom correspondence should be addressed.

Academic Editor: Michael S. Carolan

Sustainability 2021, 13(12), 6588; https://doi.org/10.3390/su13126588

Received: 12 May 2021 / Revised: 3 June 2021 / Accepted: 5 June 2021 / Published: 9 June 2021

(This article belongs to the Special Issue A Sustainable Approach in Food Science and Technology Aspects)

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Abstract

A sustainable process for valorization of onion waste would need to entail preliminary sorting out of exhausted or suboptimal material as part of decision-making. In the present study, an approach for monitoring red onion skin (OS) phenolic composition was investigated through Visible Near-Short-Wave infrared (VNIR-SWIR) (350–2500 nm) and Fourier-Transform-Mid-Infrared (FT-MIR) (4000–600 cm⁻¹) spectral analyses and Machine-Learning (ML) methods. Our stepwise approach consisted of: (i) chemical analyses to obtain reference values for Total Phenolic Content (TPC) and Total Monomeric Anthocyanin Content (TAC); (ii) spectroscopic analysis and creation of OS spectral libraries; (iii) generation of calibration and validation datasets; (iv) spectral exploratory analysis and regression modeling via several ML algorithms; and (v) model performance evaluation. Among all, the k-nearest neighbors model from 1st derivative VNIR-SWIR spectra at 350–2500 nm resulted promising for the prediction of TAC (R² = 0.82, RMSE = 0.52 and RPIQ = 3.56). The 2nd derivative FT-MIR spectral fingerprint among 600–900 and 1500–1600 cm⁻¹ proved more informative about the inherent phenolic composition of OS. Overall, the diagnostic value and predictive accuracy of our spectral data support the perspective of employing non-destructive spectroscopic tools in real-time quality control of onion waste. View Full-Text

Keywords: VNIR-SWIR; FT-MIR; chemometrics; onion solid waste; natural colorant

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MDPI and ACS Style

Tziolas, N.; Ordoudi, S.A.; Tavlaridis, A.; Karyotis, K.; Zalidis, G.; Mourtzinos, I. Rapid Assessment of Anthocyanins Content of Onion Waste through Visible-Near-Short-Wave and Mid-Infrared Spectroscopy Combined with Machine Learning Techniques. Sustainability 2021, 13, 6588. https://doi.org/10.3390/su13126588

AMA Style

Tziolas N, Ordoudi SA, Tavlaridis A, Karyotis K, Zalidis G, Mourtzinos I. Rapid Assessment of Anthocyanins Content of Onion Waste through Visible-Near-Short-Wave and Mid-Infrared Spectroscopy Combined with Machine Learning Techniques. Sustainability. 2021; 13(12):6588. https://doi.org/10.3390/su13126588

Chicago/Turabian Style

Tziolas, Nikolaos, Stella A. Ordoudi, Apostolos Tavlaridis, Konstantinos Karyotis, George Zalidis, and Ioannis Mourtzinos. 2021. "Rapid Assessment of Anthocyanins Content of Onion Waste through Visible-Near-Short-Wave and Mid-Infrared Spectroscopy Combined with Machine Learning Techniques" Sustainability 13, no. 12: 6588. https://doi.org/10.3390/su13126588

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Rapid Assessment of Anthocyanins Content of Onion Waste through Visible-Near-Short-Wave and Mid-Infrared Spectroscopy Combined with Machine Learning Techniques

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