Abstract：
The healthy benefits of seaweed have increased its market demand in recent times. Quality control is crucial for seaweed to ensure the customers\' interest and the sustainable development of seaweed farming industry. This study developed a quality control method for seaweed Sargassum fusiforme, rapid and simple, using near-infrared spectroscopy (NIR) and chemometrics for the prediction of antioxidant capacity of S. fusiforme from different growth stages, S. fusiforme was distinguished according to growth stage by partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The antioxidant properties including 2,2\'-azinobis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) scavenging capacity, 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity, and ferric reducing antioxidant power (FRAP) were quantified using competitive adaptive reweighted sampling (CARS)-PLS model. Based on the spectra data preprocessed by multiplicative scatter and standard normal variate methods, the PSO-SVM models can accurately identify the growth stage of all S. fusiforme samples. The CARS-PLS models exhibited good performance in predicting the antioxidant capacity of S. fusiforme, with coefficient of determination (RP2) and root mean square error (RMSEP) values in the independent prediction sets reaching 0.9778 and 0.4018 % for ABTS, 0.9414 and 2.0795 % for DPPH, and 0.9763 and 2.4386 μmol L-1 for FRAP, respectively. The quality and market price of S. fusiforme should increase in the order of maturation < growth < seedling regarding the antioxidant property. The overall results indicated that the NIR spectroscopy accompanied by chemometrics can assist for the quality control of S. fusiforme in a more rapid and simple manner. This study also provided a customer-oriented concept of seaweed quality grading based on deep insight into the antioxidant capability of S. fusiforme at different growth stages, which is highly valuable for precise quality control and standardization of seaweed market.
摘要：
海藻的健康益处最近增加了其市场需求。质量控制对于确保客户的兴趣和海藻养殖业的可持续发展至关重要。本研究建立了海藻羊尾藻的质量控制方法，快速而简单，利用近红外光谱(NIR)和化学计量学对不同生长阶段的梭形链球菌抗氧化能力进行预测,通过偏最小二乘判别分析（PLS-DA）和粒子群优化-支持向量机（PSO-SVM）根据生长阶段对梭形进行区分。抗氧化性能包括2,2'-嗪双-3-乙基苯并噻唑啉-6-磺酸（ABTS）清除能力，2,2-二苯基-1-吡啶酰肼(DPPH)清除能力,和铁还原抗氧化能力（FRAP）使用竞争自适应重加权抽样（CARS）-PLS模型进行量化。基于乘法散射和标准正态变量方法预处理的光谱数据，PSO-SVM模型可以准确识别所有梭形链球菌样本的生长阶段。CARS-PLS模型在预测梭形链球菌的抗氧化能力方面表现出良好的性能，对于ABTS，独立预测集中的决定系数（RP2）和均方根误差（RMSEP）值达到0.9778和0.4018%，DPPH为0.9414和2.0795%，FRAP为0.9763和2.4386μmolL-1，分别。关于抗氧化性能，梭形链球菌的质量和市场价格应按照成熟<生长<幼苗的顺序增加。总体结果表明，化学计量学的近红外光谱可以更快速，更简单地帮助梭形链球菌的质量控制。本研究还提供了一个以客户为导向的概念，基于深入了解不同生长阶段的海藻的抗氧化能力，这对于海藻市场的精确质量控制和标准化具有很高的价值。
