Presenting a Multispectral Image Sensor for Quantification of Total Polyphenols in Low-Temperature Stressed Tomato...

Area of Science:

• Agricultural Science
• Plant Physiology
• Spectroscopy

Background:

• Low-temperature stress significantly impacts tomato seedling growth and quality.
• Polyphenols are crucial antioxidants affected by environmental stressors.
• Accurate monitoring of polyphenol content is vital for assessing plant health.

Purpose of the Study:

• To develop a multispectral image sensor for predicting total polyphenol content in low-temperature stressed tomato seedlings.
• To evaluate the effectiveness of different spectral resolutions (FWHM) for polyphenol prediction.
• To establish a non-destructive method for early detection of cold stress damage.

Main Methods:

• Hyperspectral imaging was employed to collect spectral data from stressed tomato seedlings.
• Spectral data with a 5 nm Full Width at Half Maximum (FWHM) were processed to achieve FWHMs of 10 nm, 25 nm, and 50 nm.
• Least Absolute Shrinkage and Selection Operator (Lasso) regression models were developed using permutation importance and regression coefficients.

Main Results:

• A model using 56 bands (5 nm FWHM) achieved an R² of 0.71, RMSE of 3.99 mg/g, and RE of 9.04%.
• A simplified model with 5 bands (25 nm FWHM) at specific wavelengths yielded an R² of 0.62, RMSE of 4.54 mg/g, and RE of 10.3%.
• The study demonstrated the feasibility of using spectral data for polyphenol content prediction.

Conclusions:

• A multispectral image sensor can effectively predict total polyphenol content in tomato seedlings under low-temperature stress.
• This technology offers potential for energy savings and prevention of cold stress damage in vegetable production.
• The findings pave the way for developing advanced crop monitoring systems.