Study of golden pompano (Trachinotus ovatus) freshness forecasting method by utilising Vis/NIR spectroscopy combined with electronic nose

Abstract

Golden pompano (Trachinotus ovatus) quality forecasting method utilising Vis/ NIR spectroscopy combined with electronic nose (EN) was investigated in this article. Responses of Vis/NIR spectroscopy and EN to pompanos stored at 4°C were measured for 6 days. Physical/chemical indexes including texture, total volatile basic nitrogen, pH, total viable counts, and human sensory evaluation were synchronously examined as quality references. Chemometric methods including principal component analysis (PCA) and stochastic resonance (SR) were employed for spectroscopic and EN data analysis. Physicochemical examination demonstrated that fish quality decreased rapidly during storage. PCA qualitatively classified freshness degree of pompano samples, while SR signalto-noise ratio (SNR) spectrum using SNR maximum quantitatively characterised quality for all samples. Golden pompano quality predictive models were developed based on spectroscopy, EN, and spectroscopy combined with EN, respectively. Results demonstrated that the model developed based on spectroscopy combined with EN presented a forecasting accuracy of 93.3%. ARTICLE HISTORY

Evaluation of Smart Portable Device for Food Diagnostics: A Preliminary Study on Cape Hake Fillets (M. capensis and M. paradoxus)

“…Electronic noses, which have already been tested considerably in various fields [7][8][9][10][11][12][13][14][15], provide a rapid and predictive response and represent a valid support compared to the traditional more time-consuming laboratory methods.…”

The preservation effect and mechanism of gelatin on golden pompano (Trachinotus blochii) fillets during cold storage

“…Complex biological reactions still take place in the bodies of dead fish. The pH of fish gradually decreases with the accumulation of lactic acid caused by biophysiological activities in the rigour stage (Zhang et al, 2018). Subsequently, the fish becomes soft and enters the stage of decay and autolysis until ATP is completely decomposed.…”

Comparison of mutton freshness grade discrimination based on hyperspectral imaging, near infrared spectroscopy and their fusion information

“…Recently, many late‐model techniques, such as computer vision, spectroscopic, electronic nose, and multi‐source information fusion, have been widely used to detect meat freshness. Among them, near‐infrared spectroscopy (NIRS, 900–2,500 nm) and hyperspectral imaging (HSI, 400–1,000 nm) have been widely used to detect the freshness of chicken (Khulal, Zhao, Hu, & Chen, 2016), mutton (Jiang et al, 2018; Qiu et al, 2018 ), duck (Qiao et al, 2017), pork (Lee, Kim, Lee, & Cho, 2018; Qu et al, 2018), beef (Ma et al, 2012; Crichton et al, 2017), fish (Huang et al, 2014; Zhang et al, 2018) and other meat products due to their advantages of rapidity, non‐destructiveness and no pollution in the process of detecting meat products. In addition, multi‐source information fusion technique has attracted great attention of domestic and foreign researchers recently.…”

“…Wu, Yang, Zhou, Lai, and Zhong (2019) developed an improved non‐destructive measurement method for salmon freshness based on spectral and image information fusion, which effectively improved the accuracy of salmon freshness prediction model. Zhang et al (2018) studied golden pompano freshness based on the fusion of Vis/NIR spectroscopy and electronic nose, which presented a forecasting accuracy of 93.3%. Khulal, Zhao, Hu, and Chen (2017) evaluated the TVB‐N content in chicken based on the fusion model of colorimetric sensor and HSI, the results showed that multiple sensor fusion and the improved multiple level data fusion technique had better model performance to evaluate chicken meat freshness.…”