| 王岩,吴彪,刘志鸿,陈夕,于涛,王振原,孙秀俊,周丽青,郑言鑫.近江牡蛎冻干组织糖原含量近红外模型的建立.渔业科学进展,2023,44(1):210-218 |
| 近江牡蛎冻干组织糖原含量近红外模型的建立 |
| Establishment of near-infrared model for glycogen content in freeze-dried tissues of Jinjiang oyster Crassostrea ariakensis |
| 投稿时间：2021-07-31 修订日期：2021-08-29 |
| DOI： |
| 中文关键词: 近江牡蛎 近红外模型 冻干组织 糖原含量 |
| 英文关键词: Crassostrea ariakensis Near infrared model Freeze-dried tissue Glycogen content |
| 基金项目: |
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| 摘要点击次数: 680 |
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| 中文摘要: |
| 糖原直接影响牡蛎的风味和营养品质，常作为评价牡蛎品质质量的重要标准。近红外光谱(NIR)模型可以实现糖原含量的快速准确检测。本研究以近江牡蛎(Crassostrea ariakensis)为研究对象，采用微量蒽酮法、近红外技术分别获取了外套膜、鳃、闭壳肌、肝胰腺、唇瓣、性腺等6个组织及全软体部混样共909份样品的糖原含量及光谱数据，结合最小二乘法建立了近江牡蛎6个组织及全软体部的糖原含量预测模型，并对该模型进行了外部验证和交叉验证。结果显示，所测样品光谱数据经一阶求导、乘法散射校正及平滑预处理后，建立的模型最优；所建立的7个模型的建模相关系数(RC)为0.971 6~0.996 3，其外部验证相关系数(REV)及交叉验证相关系数(RCV)分别为0.949 0~0.990 8和0.969 4~0.996 9，且模型交叉验证和外部验证的RPD值均大于2.5，表明所建立的模型能准确预测近江牡蛎相应组织样品的糖原含量。本研究建立的近江牡蛎糖原含量NIR分析模型，不仅丰富了牡蛎糖原含量检测方法的研究资料，还为实现近江牡蛎糖原成分的快速、准确测定提供了技术支撑，在近江牡蛎品质性状改良等领域具有重要的应用价值。 |
| 英文摘要: |
| With the continuous improvement of living standards and expansion of oyster consumer groups, people are pursuing high-quality oysters, especially with fresh and sweet taste traits. At present, almost all varieties of oysters with different flavors entering the upscale oyster market in China are imported, and their prices are high. Before 2019, among the new oyster varieties approved in China, the dominant traits of most varieties were reflected in growth, shell color, etc. The oyster farming industry has also primarily focused on quantity but not quality, and the contradiction between scale and quality has become increasingly prominent. It can be predicted that with the development of the shellfish industry, the market demand for high shellfish quality will be more and diversified. In addition to pursing traditional traits such as growth rate, the demand for high-quality oysters, such as those with high glycogen, will be more urgent. Glycogen directly affects oysters´ flavor and nutritional quality and is often used as an important criterion to evaluate oyster quality. The efficient, rapid, and high-throughput method for determining glycogen content can provide technical support for cultivating new shellfish species with a high glycogen content. At present, the detection methods for glycogen content are mainly traditional chemical detection techniques and kits. Although these methods have been well developed, they are time-consuming and costly, producing a significant amount of chemical waste liquid. Therefore, they are not suitable for rapid and batch determination of glycogen content in large quantities. As a common modern, fast, and efficient analysis technology, near-infrared (NIR) technology can record the frequency doubling and frequency absorption of the main hydrogen-containing groups using an NIR spectrometer. NIR technology can determine large quantities of samples and has the advantages of being fast and efficient, time-saving, and labor-saving, with no chemical waste liquid generation in the experimental process, offering green environmental protection. After testing by NIR technology, the chemical properties of the samples do not change and do not require significant sample amounts, which can be used for subsequent recycling. The NIR scanning samples used in this experiment can be quickly recovered and stored in a refrigerator after obtaining spectral data through a short period of NIR scanning without affecting the use of subsequent samples. In general, the NIR technique has many advantages in determining glycogen content, and are applicable for improving oyster quality traits. This method has many advantages, such as a wide application range, and is fast, efficient, convenient, and accurate; it has been widely applied in aquatic science research. This is especially true in quality element detection research. NIR quantitative models of water, fat, glycogen, total protein, amino acid, taurine, zinc, selenium, and calcium in Crassostrea glomerata, C. angulate, Saccostrea glomerata, and C. virginica were established, with demonstrably high accuracy. Among them, NIR analysis technology has been successfully applied to breeding a new Crassostrea gigas species “Lu Yi 1,” significantly improving oyster breeding efficiency. Therefore, NIR assays can effectively overcome problems of chemical detection methods, which are time-consuming, laborious, and expensive; this is highly significant for breeding oysters with high-quality traits. The NIR spectroscopy model can be used to quickly and accurately predict glycogen content. Using C. ariakensis as the research object, the glycogen content of 909 samples in seven tissues including mantle, gill, adductor muscle, hepatopancreas, labial palps, gonad, and most soft oyster parts were determined by the micro-reaction system and method of oyster glycogen content. The corresponding spectral data were obtained using a Fourier NIR spectrometer. The spectral data and glycogen content data were analyzed and processed using TQ Analyst software, and NIR quantitative models of six tissues and all the soft parts of oysters near the Jiang River were established, and 1/9 samples of the total sample size were randomly selected for external validation and cross-validation of the models. The results showed that the measured glycogen content ranged from 7.11 to 602.20 mg/g, which had a wide range and was suitable to establish the model. This study aimed to establish a NIR model for the freeze-dried tissues of C. ariakensis to realize the rapid and accurate detection of glycogen content. This study obtained the glycogen content and spectral data of 909 samples using the micro-reaction system method and NIR technology. Combined with the least-squares method, the glycogen content prediction model of six tissues and the whole soft body of C. ariakensis was established and verified. Results also show that the model is optimal after the first derivative, multiplication scattering, and smoothing pretreatment of the measured spectral data. The modeling correlation coefficients (RC) of the seven models ranged from 0.971 6 to 0.996 3, indicating that the predicted values of the seven models were highly correlated with the actual chemical values. The correlation coefficients of external validation (REV) and cross-validation (RCV) were between 0.949 0~ 0.990 8 and 0.969 4~0.996 9, respectively, indicating that the established model could accurately predict the glycogen content of the corresponding tissue samples of C. ariakensis. This method rapidly and accurately determines the glycogen content of oysters and has application value in the field of improvement of oyster characteristics and quality. |
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