Discrimination of individual seed viability by using the oxygen consumption technique and headspace-gas chromatography-ion mobility spectrometry

Identifying and selecting high-quality seeds is crucial for improving crop yield.The purpose of this study was to improve the selection of crop seeds based on separating vital seeds from dead seeds,by predicting the potential germination ability of each seed,and thus improving seed quality.The methods of oxygen consumption (Q) of seeds and the headspace-gas chromatography-ion mobility spectrometry(HS-GC-IMS) were evaluated for identifying the viability of individual seeds.Firstly,the oxygen consumption technique showed clear differences among the values related to respiratory characteristics for seeds that were either vital or not,and the discrimination ability of final oxygen consumption(Q_(120)) was achieved not only in sweet corn seeds but also in pepper and wheat seeds.Besides,Qtwas established as a new variable to shorten the measuring process in the Q2 (oxygen sensor) procedure,which was significantly related to the viability of individual seeds.To minimize seed damage during measurement,the timing for viability evaluation was pinpointed at the 12,6 and 9 h for pepper,sweet corn,and wheat seeds based on the new variables concerning oxygen consumption (i.e.,Q_(12),Q_(6)and Q_(9),respectively).The accuracies of viability prediction were 91.9,97.7 and 96.2%,respectively.Dead seeds were identified and hence discarded,leading to an enhancement in the quality of the seed lot as indicated by an increase in germination percentage,from 86.6,90.9,and 53.8%to all at 100%.We then used the HS-GC-IMS to determine the viability of individual sweet corn seeds,noting that corn seed has a heavier weight so the volatile gas components are more likely to be detected.A total of 48 chromatographic peaks were identified,among which 38 target compounds were characterized,including alcohols,aldehydes,acids and esters.However,there were no significant differences between the vital and dead seeds,due to the trace amount volatile composition differences among the individual seeds.Furthermore,a PCA based on the signal intensities of the target volatile compounds obtained was found to lose its effectiveness,as it was unable to distinguish those two types of sweet corn seeds.These strategies can provide a reference for the rapid detection of single seed viability.

Selection for high quality pepper seeds by machine vision and classifiers

This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds. Past research has shown that seed vigor is significantly related to the seed color and size, thus several physical features were identified as candidate predictors of high seed quality. Image recognition software was used to automate recognition of seed feature quality using 400 kernels of pepper cultivar 101. In addition, binary logistic regression and a neural network were applied to determine models with high predictive value of seed germination. Single-kernel germination tests were conducted to validate the predictive value of the identified features. The best predictors of seed vigor were determined by the highest correlation observed between the physical features and the subsequent fresh weight of seedlings that germinated from the 400 seeds. Correlation analysis showed that fresh weight was significantly positively correlated with eight physical features： three color features （R, a＊, brightness）, width, length, projected area, and single-kernel density, and weight. In contrast, fresh weight significantly negatively correlated with the feature of hue. In analyses of two of the highest correlating single features,＇ germination percentage increased from 59.3 to 71.8% when a＊〉3, and selection rate peaked at 57.8%. Germination percentage increased from 59.3 to 79.4%, and the selection rate reached 76.8%, when single-kernel weight 〉0.0064 g. The most effective model was based on a multilayer perceptron （MLP） neural network, consisting of 15 physical traits as variables, and a stability calculated as 99.4%. Germination percentage in a calibration set of seeds was 79.1% and the selection rate was 90.0%. These results indicated that the model was effective in predicting seed germination based on physical features and could be used as a guide for quality control in seed selection. Automated systems based on machine vision and model classifiers can contribute to reducing the costs and labor required in the selection of pepper seeds.

同一年份同一地区不同制种公司生产的京科968玉米种子质量对比

以8家种子公司2020年在甘肃省张掖地区生产加工的京科968玉米种子为材料,检测标准发芽率和冷浸发芽率。结果表明,不同公司生产的京科968玉米种子标准发芽率均在95%以上,达到单粒播标准(93%),批次间无显著差异;冷浸发芽率在不同公司之间的差异达显著水平,有5个批次的种子冷浸后发芽率显著降低。进一步分析检测到多个与冷浸发芽率显著相关的快检指标,其中,R_同质性和B_同质性与冷浸发芽率正相关,相关系数达0.7以上;B_相异度、裂纹率、龟裂纹率与冷浸发芽率显著负相关,相关系数分别为-0.728、-0.807、-0.717。对粒型、尺寸、单粒体积、单粒比重和单粒重进行比较,发现不同公司生产的京科968玉米种子在上述指标上均存在显著差异。K-均值聚类表明,有3个公司生产的京科968整齐度较高。