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Non-Destructive Quality Monitoring of Flaxseed During Storage

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Abstract

Fluctuating environmental conditions during storage and transportation significantly influence the quality parameters. Considering over 80% of Canadian flax is exported, the industry can significantly benefit from non-destructive, rapid and reliable techniques for quality assessment at points of trade. This study was aimed at classification of flaxseed based on different storage conditions followed by prediction of quality parameters during a storage period of 16-weeks. To this aim, principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and partial least squares regression (PLSR) models were developed in the visible to near infrared (Vis-NIR) (450–1100 nm) and short-wave infrared (SWIR) (1000–2500 nm) ranges. Flaxseed samples possessing initial moisture content (MC) of 7, 8, 9, and 13% were stored at 10, 20, and 30 °C under 54, 65, 75, and 94% relative humidity (RH) conditions. Unsupervised PCA models were used for initial data exploration. PLS-DA model for storage time-based classification yielded non-error rates of 77.6, 72.2, and 77.4% in calibration, cross-validation, and external prediction, respectively. For the classification of flaxseed based on initial MC, the PLS-DA model yielded classification accuracies of 81.5, 72.8, and 87.5% in calibration, cross-validation, and external prediction, respectively. The PLSR prediction model for MC yielded a coefficient of determination (\({R}_{c}^{2}\)) of 0.96, in cross-validation (\({R}_{cv}^{2}\)) of 0.95 and in prediction (\({R}_{p}^{2}\)) of 0.97 with root mean square error in calibration (RMSEC) of 0.34, RMSECV of 0.38, and RMSEP of 0.27. Free fatty acid (FFA) value was predicted with an \({R}_{p}^{2}\) of 0.76 and a RMSEP of 6.44. Hence, it was concluded that NIRS can be used by traders, food processors, and farmers as an effective tool to non-destructively assess flaxseed quality for making logistical decisions.

Highlights

PLS-DA model classified flaxseed with a non-error rate of 77.4% in prediction.

Non-error rate for the moisture based classification of flaxseed using PLS-DA was 87.5%.

PLSR models yielded reliable prediction of moisture content with (\({R}_{p}^{2}\)) of 0.97

FFA prediction models yielded an \({R}_{p}^{2}\) of 0.76

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Acknowledgements

The authors thank Saskatchewan Flax Development Commission (SaskFlax) and Mitacs for providing financial support and Canada Foundation for Innovation for providing infrastructural support to this project.

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Authors and Affiliations

  1. Biosystems Engineering, University of Manitoba, R3T 2N2, Winnipeg, Canada

    Sristi Mundhada, Muhammad Mudassir Arif Chaudhry, Chyngyz Erkinbaev & Jitendra Paliwal

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  1. Sristi Mundhada
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Correspondence to Jitendra Paliwal.

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Mundhada, S., Chaudhry, M.M.A., Erkinbaev, C. et al. Non-Destructive Quality Monitoring of Flaxseed During Storage. Food Measure 16, 3640–3650 (2022). https://doi.org/10.1007/s11694-022-01464-5

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