NIR spectroscopy prediction model for capsaicin content estimation in chilli: A rapid mining tool for trait-specific germplasm screening

Abstract

Chilli is a widely produced crop, highly valued for its capsaicin content, a key economic trait. Traditional wet chemistry methods for estimating capsaicin are time-taking and laborious, while non-destructive methods like NIRS coupled with chemometrics, offer efficient alternatives, simplifying and accelerating biochemical assessments. This study is the first to develop and validate and tested for applicability of NIRS-based prediction model for capsaicin content in Indian chilli germplasm using MPLS regression. Various mathematical treatments were performed, and the most suited model was selected based on high RSQₑₓₜₑᵣₙₐₗ, RPD and lower SEP values in the external validation set, indicating strong prediction accuracy and minimal error. The model achieved high RSQₑₓₜₑᵣₙₐₗ value of 0.808, RPD value of 2.088 and low SEP value of 3.415 for capsaicin content, demonstrating excellent prediction performance. A paired sample t-test p-value of 0.757 (p > 0.05) showed non-significant difference between wet lab and predicted values, confirming the model’s accuracy. The applicability of the model was validated on fresh harvest germplasm the following year, showing a higher reliability score of 0.949, further confirming model’s reliability. This model would aid in high-throughput, accurate screening of chilli germplasm for capsaicin, accelerating chilli crop improvement programs and the development of new high-capsaicin varieties.

Plant phenotyping relevance

唐辛子果実のカプサイシン含量という植物形質をNIRSで非破壊推定する予測モデルを開発し、外部検証と翌年 germplasm での適用検証まで行っており、形質取得法が研究の中心である。

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