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R Soc Open Sci
2018 Jan 01;51:170714. doi: 10.1098/rsos.170714.
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Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods.
Guo X
,
Cai R
,
Wang S
,
Tang B
,
Li Y
,
Zhao W
.
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Sea cucumber is the major tonic seafood worldwide, and geographical origin traceability is an important part of its quality and safety control. In this work, a non-destructive method for origin traceability of sea cucumber (Apostichopus japonicus) from northern China Sea and East China Sea using near infrared spectroscopy (NIRS) and multivariate analysis methods was proposed. Total fat contents of 189 fresh sea cucumber samples were determined and partial least-squares (PLS) regression was used to establish the quantitative NIRS model. The ordered predictor selection algorithm was performed to select feasible wavelength regions for the construction of PLS and identification models. The identification model was developed by principal component analysis combined with Mahalanobis distance and scaling to the first range algorithms. In the test set of the optimum PLS models, the root mean square error of prediction was 0.45, and correlation coefficient was 0.90. The correct classification rates of 100% were obtained in both identification calibration model and test model. The overall results indicated that NIRS method combined with chemometric analysis was a suitable tool for origin traceability and identification of fresh sea cucumber samples from nine origins in China.
Figure 1. Sampling locations of A. japonicus from the Bohai Sea, Yellow Sea and East China Sea.
Figure 2. Original (a), second derivative after Savitsky–Golay smoothing with a window size of 17 points pre-processed (b) and second derivative coupled vector normalization pre-processed (c) NIR diffuse reflectance spectra of A. japonicus samples.
Figure 3. Correlation between the NIRS predicted values and the reference values of total fat in A. japonicus samples by PLS in Step I quantitative model (148 samples in calibration set, in black; 41 samples in test set, in red).
Figure 4. The flow chart of procedures for the ‘two-step’ identification model establishment. Samples in green blocks represent unambiguous classified and samples in red block represent ambiguous classified. XP, Xiapu; LZ, Laizhou; YT, Yantai; RS, Rushan; QD, Qingdao; WFD, Wanfangdian; PLD, Pulandian; CH, Changhai; ZZ, Zhangzi Island.
Figure 5. Score plots with three principal components (PCs) for Step I calibration set samples from nine origins including Changhai (CH), Wafangdian (WFD), ZhangziIsland (ZZ), Pulandian (PLD), Qingdao (QD), Rushan (RS), Yantai (YT), Laizhou (LZ), Xiapu (XP), which were obtained by second derivative after Savitsky–Golay smoothing with a window size of 17 points pre-processed.
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