Search

Raman spectroscopy was compared with near infrared (NIR) hyperspectral imaging for determination of fat composition (%EPA + DHA) in salmon fillets at short exposure times. Fillets were measured in movement for both methods. Salmon were acquired from several different farming locations in Norway with different feeding regimes, representing a realistic variation of salmon in the market. For Raman, we investigated three manual scanning strategies; i) line scan of loin, ii) line scan of belly and iii) sinusoidal scan of belly at exposure times of 2s and 4s. NIR images were acquired while the fillets moved on a conveyor belt at 40 cm/s, which corresponds to an acquisition time of 1s for a 40 cm long fillet. For NIR images, three different regions of interest (ROI) were investigated including the i) whole fillet, ii) belly segment, and iii) loin segment. For both Raman and NIR measurements, we investigated an untrimmed and trimmed version of the fillets, both relevant for industrial in-line evaluation. For the trimmed fillets, a fat rich deposition layer in the belly was removed. The %EPA + DHA models were validated by cross validation (N = 51) and using an independent test set (N = 20) which was acquired in a different season. Both Raman and NIR showed promising results and high performances in the cross validation, with R2 CV = 0.96 for Raman at 2s exposure and R2 CV = 0.97 for NIR. High performances were obtained also for the test set, but while Raman had low and stable biases for the test set, the biases were high and varied for the NIR measurements. Analysis of variance on the squared test set residuals showed that performance for Raman measurements were significantly higher than NIR at 1% significance level (p = 0.000013) when slope-and-bias errors were not corrected, but not significant when residuals were slope-and-bias corrected (p = 0.28). This indicated that NIR was more sensitive to matrix effects. For Raman, signal-to-noise ratio was the main limitation and there were indications that Raman was close to a critical sample exposure time at the 2s signal accumulation. [Display omitted] • Feasibility of in-line estimation of fatty acid features (%EPA + DHA) in salmon fillets. • Raman spectroscopy was compared with near infrared hyperspectral imaging (NIR). • Promising results for both methods. • Indications that shorter exposure times than 2s might be challenging for Raman. • Raman gave low and stable biases (test set). NIR gave high and varied biases. [ABSTRACT FROM AUTHOR]

It is important to utilize the entire animal in meat and fish production to ensure sustainability. Rest raw materials, such as bones, heads, trimmings, and skin, contain essential nutrients that can be transformed into high-value products. Enzymatic protein hydrolysis (EPH) is a bioprocess that can upcycle these materials to create valuable proteins and fats. This paper focuses on the role of spectroscopy and chemometrics in characterizing the quality of the resulting protein product and understanding how raw material quality and processing affect it. The article presents recent developments in chemical characterisation and process modelling, with a focus on rest raw materials from poultry and salmon production. Even if some of the technology is relatively mature and implemented in many laboratories and industries, there are still open challenges and research questions. The main challenges are related to the transition of technology and insights from laboratory to industrial scale, and the link between peptide composition and critical product quality attributes. • Spectroscopy and chemometrics are crucial tools for understanding bioprocesses. • Raw material properties and process settings both affect the end-product quality. • NIR and Raman spectroscopy may be used for in-line characterisation of raw materials. • FTIR, SEC and NMR provide detailed characterisation of protein hydrolysates. • Transition of technology and insights from lab to industry is a remaining barrier. [ABSTRACT FROM AUTHOR]

The interaction between near infrared (NIR) light and the sample is a key factor to take into consideration when developing applications using NIR spectroscopy. The aim of this study was to assess the penetration depth of the NIR light through both intact watermelons and the red flesh of the cut fruit, using an interactance NIR system. The results showed a maximum penetration of between 9 and 11 mm in the assays carried out on intact watermelons and 11 mm in the watermelon flesh. The prediction performance of the different NIR system configurations used were compared by quantifying the SSC in intact watermelons in a non-destructive way. The results obtained for the assay in which the illuminated regions were separated by 28 mm (Distance 4) reported the best results for determination of SSC (%) obtaining an R2 cv = 0.73 and RMSECV = 0.39 % and an R2 p = 0.81 and RMSEP = 0.30 % when the set of data for that distance was split into calibration and internal validation sets. • The penetration depth of NIR light into different watermelons tissues was studied. • The penetration depth of NIR light was assessed using MCR-ALS. • The optimum optical measurement geometry of the NIR system was identified. • Penetration depth through the rind of the watermelon was about 9–11 mm. • NIR interactance spectroscopy was used to measure the SSC in intact watermelons. [ABSTRACT FROM AUTHOR]

Raman, near-infrared and fluorescence spectroscopy were evaluated for determination of collagen content in ground meat. Two sample sets were used (i.e. ground beef and ground poultry by-products), and collagen concentrations (measured as hydroxyproline) varied in the ranges 0.1–3.3% in the beef samples and 0.4–1.5% in the poultry samples. Similar validation results for hydroxyproline were obtained for NIRS (R2 = 0.82 and RMSECV = 0.11%) and Raman (R2 = 0.81 and RMSECV = 0.11%) for the poultry samples. For the beef samples, NIRS obtained slightly less accurate results (R2 = 0.89, RMSECV = 0.25%) compared to Raman (R2 = 0.94, RMSECV = 0.19%), most likely due to less representative sampling. Fluorescence spectroscopy gave higher prediction errors (RMSECV = 0.50% and 0.13% for beef and poultry, respectively). This shows that Raman spectroscopy employing a scanning approach for representative sampling is a potential tool for on-line determination of collagen in meat. • Non-destructive determination of collagen in meat products by Raman spectroscopy. • Regression models were compared for different spectroscopic techniques. • Specific models for collagen and proteins are possible with Raman spectroscopy. [ABSTRACT FROM AUTHOR]