Abstract Biological variability is the natural characteristic of agricultural products. Non-destructive determination of fruit/vegetable soluble solid content (SSC) using spectral detection method is still a challenge due to the spectral variation caused by abundant biological variations, such as different cultivars, geographic origins and harvest seasons. In this paper, a self-adaptive model was established by combining five correcting methods for biological variability elimination, self-selection strategy and model search technology. Thus, the model can automatically adapt to the change of diverse biological variation compared to others. Furthermore, 100 cycles of selection accompanied with the random algorithm were set up to randomly select the calibration sets and prediction sets to ensure the reliability of the results. For the same batch of blueberry samples, five correcting models showed different prediction performances and all achieved satisfactory prediction accuracy compared to the individual-variation model and the hybrid-variation model. The consequence of the self-adaptive model showed consistency when considering multiple variation as well as variation with only cultivars or seasons. The best models in the three cases (multiple variation, only cultivars and only seasons) were all based on the preprocessing method, which was selected for 70, 57 and 47 times respectively. The results indicated that the biological variability had an impact on SSC prediction and that correcting models could improve the prediction accuracy. For the blueberry samples, the most suitable model selected according to the adaptive results was the preprocessing-based model. Within the study conditions, the self-adaptive model can select the most reliable model with the best prediction performance with respect to different variations.

中文翻译：

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利用近红外光谱和化学计量学预测具有生物变异性的蓝莓可溶性固形物含量的自适应模型

摘要 生物变异性是农产品的自然特性。由于品种、地理来源和收获季节等丰富的生物变异导致光谱变化，使用光谱检测方法无损测定水果/蔬菜可溶性固形物含量（SSC）仍然是一个挑战。本文结合生物变异性消除、自选择策略和模型搜索技术五种校正方法，建立了自适应模型。因此，与其他模型相比，该模型可以自动适应各种生物变异的变化。此外，设置100个循环的选择伴随着随机算法，随机选择校准集和预测集，以确保结果的可靠性。对于同一批蓝莓样本，5个校正模型表现出不同的预测性能，与个体变异模型和混合变异模型相比均取得了令人满意的预测精度。自适应模型的结果在考虑多重变异以及仅与品种或季节的变异时表现出一致性。三种情况下（多变异、仅品种、仅季节）的最佳模型均基于预处理方法，分别选择了70、57和47次。结果表明，生物变异性对SSC预测有影响，修正模型可以提高预测精度。对于蓝莓样本，根据自适应结果选择最合适的模型是基于预处理的模型。