Large-Scale Screening of Intact Tomato Seeds for Viability Using Near Infrared Reflectance Spectroscopy (NIRS)

A Rapid and Quantitative Method for Determining Seed Viability Using 2,3,5-Triphenyl Tetrazolium Chloride (TTC): With the Example of Wheat Seed

Current colorimetric methods for quantitative determination of seed viability (SV) with 2,3,5-triphenyl tetrazolium chloride (TTC) have been plagued by issues of being cumbersome and time-consuming during the experimental process, slow in extraction and staining, and exhibiting inconsistent results. In this work, we introduced a new approach that combines TTC-staining with high-temperature extraction using dimethyl sulfoxide (DMSO). The optimization of the germination stage, TTC-staining method, and 1,3,5-triphenylformazan (TTF) extraction method were meticulously carried out as follows: When the majority of wheat seeds had grown the radicle, and the length of radicles was approximately equal to the seed length (24 h-germination), 2 g germinating seeds were placed into a beaker (20 mL) containing 5 mL 10 g·L-1 TTC solution. The seeds were stained with TTC in the dark at 25 °C for 1 h. Following the staining, 1 mL 1 mol·L-1 H2SO4 was added to stop the reaction for 5 min. The H2SO4 solution was then removed, and the seeds were gently rinsed with deionized water. Subsequently, the TTF produced in the seeds was extracted directly with 5 mL DMSO solution at 55 °C for 1 h. The absorbance of the extract was measured at 483 nm, and the index of SV was calculated according to a predetermined TTC calibration curve and expressed by mg TTC·g-1 (seed)·h-1. The new method has been demonstrated to be rapid, stable, and highly sensitive, as evidenced by the accurate measurement of seed viability with different aging degrees.

Classification Method for Viability Screening of Naturally Aged Watermelon Seeds Using FT-NIR Spectroscopy

Viability analysis of stored seeds before sowing has a great importance as plant seeds lose their viability when they exposed to long term storage. In this study, the potential of Fourier transform near infrared spectroscopy (FT-NIR) was investigated to discriminate between viable and non-viable triploid watermelon seeds of three different varieties stored for four years (natural aging) in controlled conditions. Because of the thick seed-coat of triploid watermelon seeds, penetration depth of FT-NIR light source was first confirmed to ensure seed embryo spectra can be collected effectively. The collected spectral data were divided into viable and nonviable groups after the viability being confirmed by conducting a standard germination test. The obtained results showed that the developed partial least discriminant analysis (PLS-DA) model had high classification accuracy where the dataset was made after mixing three different varieties of watermelon seeds. Finally, developed model was evaluated with an external data set (collected at different time) of hundred samples selected randomly from three varieties. The results yield a good classification accuracy for both viable (87.7%) and nonviable seeds (82%), thus the developed model can be considered as a “general model” since it can be applied to three different varieties of seeds and data collected at different time.

Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis

The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR) spectroscopy with a wavelength range of 1000-2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA) were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.