Henning S
Department of Food Technology and Science, Faculty of Applied Sciences, Cape Peninsula University of Technology, Bellville, South Africa.
J Fish Dis. 2025 Jan;48(1):e14025. doi: 10.1111/jfd.14025. Epub 2024 Oct 6.
Kudoa thyrsites infection of marine fish typically results in myoliquefaction, which is only apparent 24 to 56 h post-mortem. The traditional methods for the detection of K. thyrsites infected fish are time-consuming and destructive, reducing its marketability. This poses a challenge for the fish industry to remove infected fish before it reaches the market or further processing activities. This study investigated the use of near-infrared (NIR) spectroscopy, in combination with soft independent modelling of class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA), for discriminating K. thyrsites infected fish from uninfected fish. Performance of the classification models was evaluated by calculating the sensitivity, specificity and precision. A total of 334 fish samples (200 sardine, 64 hake and 70 kingklip) were used for this study. Infection of K. thyrsites was determined with the use of qPCR assays. Ninety per cent (90%) of the sardine samples, 78% of the hake samples and 37% of the kingklip samples were infected. Class groups of infected and uninfected fish samples were created for the purpose of generating SIMCA and PLS-DA classification models for each species of fish, as well as for a species independent data set. Principal component analysis (PCA) of NIR spectra did not show any clustering for infected and uninfected samples. Calibration and test sample sets were generated for the purpose of building and testing the SIMCA and PLD-DA classification models. SIMCA and PLS-DA were unable to classify test samples correctly into the two classes. The number of misclassifications (NMC) was higher for the SIMCA models than for the PLS-DA models, with more than 60% incorrectly classified. SIMCA classified most of the test samples into both classes. The precision for PLS-DA were 89% for sardine, 81% for hake, 0% for kingklip and 87% for species independent models, however, most samples were classified at infected. The use of NIR spectroscopy and classification models such as SIMCA and PLS-DA showed limited use as a method to distinguish between K. thyrsites infected and uninfected fish samples. Textural and chemical changes during extended frozen storage of the fish samples may have masked the effects associated with K. thyrsites infection. Further studies are suggested where NIR spectroscopy is used in combination with texture analysis and image spectroscopy.
海洋鱼类感染 Thyrsites 库道虫通常会导致肌液化,这仅在死后 24 至 56 小时才会显现。检测感染 Thyrsites 库道虫鱼类的传统方法既耗时又具破坏性,降低了其市场价值。这给鱼类产业带来了挑战,即在感染鱼进入市场或进行进一步加工活动之前将其剔除。本研究调查了近红外(NIR)光谱法结合类相关软独立建模(SIMCA)和偏最小二乘判别分析(PLS - DA),用于区分感染 Thyrsites 库道虫的鱼和未感染的鱼。通过计算灵敏度、特异性和精确度来评估分类模型的性能。本研究共使用了 334 个鱼样本(200 条沙丁鱼、64 条无须鳕和 70 条金头鲷)。使用 qPCR 检测法确定 Thyrsites 库道虫的感染情况。90%的沙丁鱼样本、78%的无须鳕样本和 37%的金头鲷样本被感染。为了为每种鱼类以及一个独立于物种的数据集生成 SIMCA 和 PLS - DA 分类模型,创建了感染和未感染鱼样本的类别组。近红外光谱的主成分分析(PCA)未显示感染和未感染样本有任何聚类情况。为构建和测试 SIMCA 和 PLD - DA 分类模型生成了校准和测试样本集。SIMCA 和 PLS - DA 无法将测试样本正确分类到两个类别中。SIMCA 模型的错误分类数量(NMC)高于 PLS - DA 模型,超过 60%被错误分类。SIMCA 将大多数测试样本分类到了两个类别中。PLS - DA 对沙丁鱼的精确度为 89%,对无须鳕为 81%,对金头鲷为 0%,对独立于物种的模型为 87%,然而,大多数样本被分类为感染。使用近红外光谱法以及 SIMCA 和 PLS - DA 等分类模型作为区分感染 Thyrsites 库道虫和未感染鱼样本的方法显示出有限的用途。鱼样本在长期冷冻储存期间的质地和化学变化可能掩盖了与 Thyrsites 库道虫感染相关的影响。建议进一步开展研究,将近红外光谱法与质地分析和图像光谱法结合使用。