Comparing Manual Counting to Automated Image Analysis for the Assessment of Fungiform Papillae Density on Human Tongue.

The density of fungiform papillae (FPD) on the human tongue is currently taken as index for responsiveness to oral chemosensory stimuli. Visual analysis of digital tongue picture and manual counting by trained operators represents the most popular technique for FPD assessment. Methodological issues mainly due to operator bias are considered among factors accounting for the uncertainty about the relationships between FPD and responsiveness to chemosensory stimuli. The present study describes a novel automated method to count fungiform papillae (FP) from image analysis of tongue pictures. The method was applied to tongue pictures from 133 subjects. Taking the manual count as reference method, a partial least squares regression model was developed to predict FPD from tongue automated analysis output. FPD from manual and automated count showed the same normal distribution and comparable descriptive statistic values. Consistent subject classifications as low and high FPD were obtained according to the median values from manual and automated count. The same results on the effect of FPD variation on taste perception were obtained both using predicted and counted values. The proposed method overcomes count uncertainties due to researcher bias in manual counting and is suited for large population studies. Additional information is provided such as FP size class distribution which would help for a better understanding of the relationships between FPD variation and taste functions.

Diagnosing the cage of covariance to increase understanding and robustness of spectroscopic calibration models.

When vibrational spectroscopy is used for quantification purposes, multivariate analysis is often used to extract information from covariances between the spectra and any given reference values. In complex samples, there is a high risk that the constituents covary with each other. In such scenarios many methods may confuse the analytes and use signal from several analytes, rather than just the analyte of interest. While this allows the method to use more signal, and thus have a better effective signal-to-noise ratio, it also makes them less robust to changes to the chemical composition in the samples. This effect has been termed the cage of covariance. In order to avoid cage of covariance to affect predictive performances, it is highly important to have simple diagnostic tools to analyze and review this effect. Therefore, in the present paper, a systematic overview of tools for diagnosing and quantifying the cage of covariance in spectroscopic calibration models is provided. A collection of previously published methods with some expansions is provided, as well as two completely new tools: covariance ratio and virtual spiking. Practical applications of the tools on three different datasets are also shown.

In-Line Near-Infrared Spectroscopy Gives Rapid and Precise Assessment of Product Quality and Reveals Unknown Sources of Variation-A Case Study from Commercial Cheese Production.

Quality testing in the food industry is usually performed by manual sampling and at/off-line laboratory analysis, which is labor intensive, time consuming, and may suffer from sampling bias. For many quality attributes such as fat, water and protein, in-line near-infrared spectroscopy (NIRS) is a viable alternative to grab sampling. The aim of this paper is to document some of the benefits of in-line measurements at the industrial scale, including higher precision of batch estimates and improved process understanding. Specifically, we show how the decomposition of continuous measurements in the frequency domain, using power spectral density (PSD), may give a useful view of the process and serve as a diagnostic tool. The results are based on a case regarding the large-scale production of Gouda-type cheese, where in-line NIRS was implemented to replace traditional laboratory measurements. In conclusion, the PSD of in-line NIR predictions revealed unknown sources of variation in the process that could not have been discovered using grab sampling. PSD also gave the dairy more reliable data on key quality attributes, and laid the foundation for future improvements.

Raman spectroscopy and NIR hyperspectral imaging for in-line estimation of fatty acid features in salmon fillets.

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 R2CV = 0.96 for Raman at 2s exposure and R2CV = 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.

The role of biospectroscopy and chemometrics as enabling technologies for upcycling of raw materials from the food industry.

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.

Characterization of wooden breast myopathy: a focus on syndecans and ECM remodeling.

Introduction: The skeletal muscle deformity of commercial chickens (Gallus gallus), known as the wooden breast (WB), is associated with fibrotic myopathy of unknown etiology. For future breeding strategies and genetic improvements, it is essential to identify the molecular mechanisms underlying the phenotype. The pathophysiological hallmarks of WB include severe skeletal muscle fibrosis, inflammation, myofiber necrosis, and multifocal degeneration of muscle tissue. The transmembrane proteoglycans syndecans have a wide spectrum of biological functions and are master regulators of tissue homeostasis. They are upregulated and shed (cleaved) as a regulatory mechanism during tissue repair and regeneration. During the last decades, it has become clear that the syndecan family also has critical functions in skeletal muscle growth, however, their potential involvement in WB pathogenesis is unknown. Methods: In this study, we have categorized four groups of WB myopathy in broiler chickens and performed a comprehensive characterization of the molecular and histological profiles of two of them, with a special focus on the role of the syndecans and remodeling of the extracellular matrix (ECM). Results and discussion: Our findings reveal differential expression and shedding of the four syndecan family members and increased matrix metalloproteinase activity. Additionally, we identified alterations in key signaling pathways such as MAPK, AKT, and Wnt. Our work provides novel insights into a deeper understanding of WB pathogenesis and suggests potential therapeutic targets for this condition.

In-Line Estimation of Fat Marbling in Whole Beef Striploins (Longissimus lumborum) by NIR Hyperspectral Imaging. A Closer Look at the Role of Myoglobin.

Fat marbling, the amount, and distribution of intramuscular fat, is an important quality trait for beef loin (Longissimus lumborum) and is closely connected to sensory properties such as tenderness, juiciness, and flavor. For meat producers, it would be of value to grade and sort whole loins according to marbling on the production line. The main goal of this study was to evaluate high-speed NIR hyperspectral imaging in interaction mode (760-1047 nm) for in-line measurement of sensory assessed marbling in both intact loins and loin slices. The NIR system was calibrated based on 28 whole striploins and 412 slices. Marbling scores were assessed for all slices on a scale from 1 to 9 by a trained sensory panel. The calibrated NIR system was tested for in-line measurements on 30 loins and 60 slices at a commercial meat producer. Satisfactory accuracy for prediction of marbling was obtained by partial least squares regression for both slices and whole loins (R2 = 0.81 & 0.82, RMSEP = 0.95 & 0.88, respectively). The concentration of myoglobin in the meat and its state of oxygenation has a strong impact on the NIR spectra and can give deviations in the estimated marbling scores. This must be carefully considered in industrial implementation.

Feasibility of In-Line Raman Spectroscopy for Quality Assessment in Food Industry: How Fast Can We Go?

Raman spectroscopy is a viable tool within process analytical technologies due to recent technological advances. In this article, we evaluate the feasibility of Raman spectroscopy for in-line applications in the food industry by estimating the concentration of the fatty acids EPA + DHA in ground salmon samples (n = 63) and residual bone concentration in samples of mechanically recovered ground chicken (n = 66). The samples were measured under industry like conditions: They moved on a conveyor belt through a dark cabinet where they were scanned with a wide area illumination standoff Raman probe. Such a setup should be able to handle relevant industrial conveyor belt speeds, and it was studied how different speeds (i.e., exposure times) influenced the signal-to-noise ratio (SNR) of the Raman spectra as well as the corresponding model performance. For all samples we applied speeds that resulted in 1 s, 2 s, 4 s, and 10 s exposure times. Samples were scanned in both heterogenous and homogenous state. The slowest speed (10 s exposure) yielded prediction errors (RMSECV) of 0.41%EPA + DHA and 0.59% ash for the salmon and chicken data sets, respectively. The more in-line relevant exposure time of 1 s resulted in increased RMSECV values, 0.84% EPA + DHA and 0.84% ash, respectively. The increase in prediction error correlated closely with the decrease in SNR. Further improvements of model performance were possible through different noise reduction strategies. Model performance for homogenous and heterogenous samples was similar, suggesting that the presented Raman scanning approach has the potential to work well also on intact heterogenous foods. The estimation errors obtained at these high speeds are likely acceptable for industrial use, but successful strategies to increase SNR will be key for widespread in-line use in the food industry.

Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue-A Salmon Case Study.

The aim of the present study was to critically evaluate the potential of using NIR and Raman spectroscopy for prediction of fatty acid features and single fatty acids in salmon muscle. The study was based on 618 homogenized salmon muscle samples acquired from Atlantic salmon representing a one year-class nucleus, fed the same high fish oil feed. NIR and Raman spectra were used to make regression models for fatty acid features and single fatty acids measured by gas chromatography. The predictive performance of both NIR and Raman was good for most fatty acids, with R2 above 0.6. Overall, Raman performed marginally better than NIR, and since the Raman models generally required fewer components than respective NIR models to reach high and optimal performance, Raman is likely more robust for measuring fatty acids compared to NIR. The fatty acids of the salmon samples co-varied to a large extent, a feature that was exacerbated by the overlapping peaks in NIR and Raman spectra. Thus, the fatty acid related variation of the spectroscopic data of the present study can be explained by only a few independent principal components. For the Raman spectra, this variation was dominated by functional groups originating from long-chain polyunsaturated FAs like EPA and DHA. By exploring the independent EPA and DHA Raman models, spectral signatures similar to the respective pure fatty acids could be seen. This proves the potential of Raman spectroscopy for single fatty acid prediction in muscle tissue.

Assessment of Bulk Composition of Heterogeneous Food Matrices Using Raman Spectroscopy.

Raman spectroscopy (RS) has for decades been considered a promising tool for food analysis, but widespread adoption has been held back by, e.g., high instrument costs and sampling limitations regarding heterogeneous samples. The aim of the present study was to use wide area RS in conjunction with surface scanning to overcome the obstacle of heterogeneity. Four different food matrices were scanned (intact and homogenized pork and by-products from salmon and poultry processing) and the bulk chemical parameters such as fat and protein content were estimated using partial least squares regression (PLSR). The performance of PLSR models from RS was compared with near-infrared spectroscopy (NIRS). Good to excellent results were obtained with PLSR models from RS for estimation of fat content in all food matrices (coefficient of determination for cross-validation (R2CV) from 0.73 to 0.96 and root mean square error of cross-validation (RMSECV) from 0.43% to 2.06%). Poor to very good PLSR models were obtained for estimation of protein content in salmon and poultry by-product using RS (R2CV from 0.56 to 0.92 and RMSECV from 0.85% to 0.94%). The performance of RS was similar to NIRS for all analyses. This work demonstrates the applicability of RS to analyze bulk composition in heterogeneous food matrices and paves way for future applications of RS in routine food analyses.

Optimization of Instrument Design for In-Line Monitoring of Dry Matter Content in Single Potatoes by NIR Interaction Spectroscopy.

Dry matter (DM) content is one of the most important quality features of potatoes. It defines the physical properties of the potatoes and determines what kind of product the potatoes can be used for. This paper presents the results obtained by a novel prototype NIR (near-infrared) instrument designed to measure DM content in single potatoes in process. The instrument is based on interaction measurements to measure deeper into the potatoes. It measures rapidly, up to 50 measurements per second, allowing several moving potatoes to be measured per second. The instrument also enables several interactance distances to be recorded for each measurement. The instrument was calibrated based on three different potato varieties and the calibration measurements were done in a process plant, making the calibration model suitable for in-line use. A good calibration for DM was obtained by partial least squares regression (RMSECV = 0.78% DM, R2 = 0.91). The instrument was tested in-line in the process plant and several batches of potatoes were monitored for the estimation of the DM distribution per batch. Accuracy of DM determination as function of measurement position on the potato was studied, and results indicate that NIR scans along the center part of the potatoes give slightly better results compared to scans taken on either side of the center. Small differences in optical measurement geometry influence the accuracy of the calibration models, underlining the importance of optimizing instrument design for successful measurements.

Prediction of water holding capacity and pH in porcine longissimus lumborum using Raman spectroscopy.

The main purpose of this study was to investigate if Raman spectra recorded at the exact same position as drip loss measurements could improve prediction of drip loss in pork. One ventral and one dorsal cylindrical plug, cut from a standardized slice from Longissimus lumborum, were used to determine drip loss by EZ-DripLoss method and to collect Raman spectra, while ultimate pH was measured at another location. Partial least squares regression models were developed using spectra from each plug individually or averaged spectra from both plugs. The best models used spectra from the ventral plug, resulting in rcv2=0.75, root mean square error of cross-validation (RMSECV) = 1.27% and ratio of prediction to deviation (RPD) =2.0 for EZ-DripLoss and rcv2=0.72, RMSECV = 0.05 and RPD = 2.0 for ultimate pH. Results indicate that Raman spectroscopy can be used for rough screening of drip loss and pH in pork, and that the location chosen for collection of spectra can be very important for successful predictions.

Effect of oxygen concentration on photo-oxidation and photosensitizer bleaching in butter.

The effect of headspace oxygen concentration and color of light on photo-oxidation and degradation of photosensitizers in butter was studied. Butter samples were stored under 0%, 0.4%, 0.8%, 1.6%, 3.0%, 5.0%, 21% oxygen, and exposed to violet, green or red light. Storage time was 36 h. Degree of photo-oxidation was measured by a trained sensory panel. Photobleaching of six different photosensitizers was estimated based on front face fluorescence excitation and emission landscapes and spectral curve resolution (parallel factor analysis). The higher oxygen concentration, the more sensory degraded were the samples. Violet light resulted in slightly higher degrees of photo-oxidation than green and red light for low oxygen concentrations. Bleaching rate and course as function of O(2) concentration differed between the photosensitizers. It is suggested that the rate of photobleaching is a balance between type I and type II photoreactions.

Classification of different bovine muscles according to sensory characteristics and Warner Bratzler shear force.

The aim was to examine how well different beef muscles from Norwegian Red bulls respond to the consumer needs. Ten carcasses were slaughtered at a commercial abbatoir, chilled at 4°C for 48h, and 10 muscles excised. After ageing for 9days at 4°C, the muscles were subjected to sensory and chemical analyses and classified according to these analyses in 4 quality groups. The results regarding the comparative quality of the muscles were similar to results from other studies on predominantly steers. M. infraspinatus showed superior tenderness, juiciness and colour properties and was the only muscle to be consistent in tenderness with 80% of the samples in the highest sensory quality class. Also M. triceps brachii and M. semimembranosus adductor were reasonably tender and consistent in tenderness. As compared with the studies on steers, the M. biceps femoris and M. vastus lateralis seemed to be less tender in bulls. Results regarding sensory colour intensity, juiciness and taste showed similar findings. The pattern of association between the muscles in this study was highly irregular as the relative muscle quality varied widely, which means that using M. longissimus dorsi as a quality indicator of all muscles in the carcass is questionable.

Near-infrared spectroscopy detects woody breast syndrome in chicken fillets by the markers protein content and degree of water binding.

The muscle syndrome woody breast (WB) impairs quality of chicken fillets and is a challenge to the poultry meat industry. There is a need for online detection of affected fillets for automatic quality sorting in process. Near-infrared spectroscopy (NIRS) is a promising method, and in this study we elucidate the spectral properties of WB versus normal fillets. On a training set of 50 chicken fillets (20 normal, 30 WB), we measured NIR, nuclear magnetic resonance (NMR) T2 relaxation distributions, and crude chemical composition. NIRS could estimate protein in the fillets with an accuracy of ±0.64 percentage points. T2 distributions showed that there was a larger share of free water in WB fillets. This difference in water binding generated a shift and narrowing of the water absorption peak in NIR around 980 nm, quantified by a bound water index (BWI). The correlation between BWI and T2 distributions was 0.78, indicating that NIRS contains information about degree of water binding. Discriminant analysis showed that NIRS obtained 100% correct classification of normal versus WB on the training set, and 96% correct classification on a test set of 52 fillets. The main reason for why NIRS can successfully discriminate between WB and normal fillets is the methods sensitivity to both protein content and degree of water binding in the muscle, both established markers for WB. The classification model can be based on NIR spectra only, calibration against protein is not needed. The affected muscle tissue associated with the WB syndrome is unevenly distributed in the fillets, and this heterogeneity was characterized by NIRS and NMR. Clear differences in water binding properties were found between the superficial 1 cm layer and the deeper layer at 1 to 2 cm depth. Significant differences in protein estimates by NIRS at different measurement points along the chicken fillets were obtained for WB fillets. The findings suggest how to obtain optimal sampling with NIRS for best possible discrimination between WB and normal breast fillets.

Non-destructive Raman spectroscopy as a tool for measuring ASTA color values and Sudan I content in paprika powder.

The aim of this study was developing a non-destructive method for the determination of color in paprika powder as well as for detecting possible adulteration with Sudan I. Non-destructive Raman spectroscopy was applied directly to paprika powder employing a laser excitation of 785 nm for the first time. The fluorescence background was estimated, by fitting a polynomial to each spectrum, and then subtracted. After preprocessing the spectra, some peaks were clearly identified as characteristic from pigments present in paprika. The preprocessed Raman spectra were correlated with the ASTA color values of paprika by partial least squares regression (PLSR). Twenty-five paprika samples were adulterated with Sudan I at different levels and the PLSR model was also obtained. The coefficients of determination (R2) were 0.945 and 0.982 for ASTA and Sudan I concentration, respectively, and the root mean square errors of prediction (RMSEP) were 8.8 ASTA values and 0.91 mg/g, respectively. Finally, different approaches were applied to discriminate between adulterated and non-adulterated samples. Best results were obtained for partial least squares - discriminant analysis (PLS-DA), allowing a good discrimination when the adulteration with Sudan I was higher than 0.5%.

Analyzing µ-Calpain induced proteolysis in a myofibril model system with vibrational and fluorescence spectroscopy.

Degree of post-mortem proteolysis influences overall meat quality (e.g. tenderness and water holding capacity). Degradation of isolated pork myofibril proteins by µ-Calpain for 0, 15 or 45 min was analyzed using four spectroscopic techniques; Raman, Fourier transform infrared (FT-IR), near infrared (NIR) and fluorescence spectroscopy. Sodium dodecyl sulfate polyacrylamide gel electrophoresis was used to determine degree of proteolysis. The main changes detected by FT-IR and Raman spectroscopy were degradation of protein backbones manifested in the spectra as an increase in terminal carboxylic acid vibrations, a decrease in CN vibration, as well as an increase in skeletal vibrations. A reduction in ß-sheet secondary structures was also detected, while α-helix secondary structure seemed to stay relatively unchanged. NIR and fluorescence were not suited to analyze degree of proteolysis in this model system.

Predicting post-mortem meat quality in porcine longissimus lumborum using Raman, near infrared and fluorescence spectroscopy.

Spectroscopic techniques can provide valuable information about post-mortem meat quality. In the current study, Raman, NIR and fluorescence spectroscopy was used to analyze pH, drip loss and intramuscular fat in pork longissimus lumborum (n = 122) at 4-5 days post-mortem. Results were promising for partial least squares regression (PLSR) from Raman spectroscopy, giving coefficients of determination from cross validation (rcv2) ranging from 0.49 to 0.73 for all attributes examined. Important regions in the PLSR models from Raman spectroscopy were attributed to changes in concentrations of post-mortem metabolites and modifications of protein secondary structure. Near infrared and fluorescence spectroscopy showed limited ability to analyze quality, with rcv2 ranging from 0.06 to 0.57 and 0.04 to 0.18, respectively. This study encourages further research on the subject of Raman spectroscopy as a technique for meat quality analysis.

A chromatographic approach for fluorescence reduction in liquid Raman analysis.

In this study a chromatographic approach for fluorescence reduction in liquid Raman analysis has been evaluated. The idea behind the approach is to apply a chromatographic separation step prior to Raman analysis in order to separate fluorescing compounds from other components of interest, thus facilitating better quantitative and qualitative analysis of the latter components. A real-time liquid-core Raman waveguide detector designed for chromatographic applications was used in the study, thus providing real-time chemical pretreatment of liquid samples for Raman analysis. Twenty aqueous mixtures of additives frequently found in beverages were analyzed, and for comparative purposes the mixtures were also analyzed in the Raman waveguide detector without chromatographic separation and with a conventional immersion probe. Both qualitatively and quantitatively satisfying results were obtained using the chromatographic Raman approach, and the technique provided possibilities for quantitative and qualitative assessments superior to the two other instrumental setups. The technique may provide additional benefits through sensitivity enhancements, and the approach is simple, inexpensive, and easy to implement in the average applied Raman laboratory. The analysis of various chemical systems and factors such as system stability over time need further evaluation in order to confirm the general applicability of the approach.

Analyzing pH-induced changes in a myofibril model system with vibrational and fluorescence spectroscopy.

The decline of pH and ultimate pH in meat postmortem greatly influences meat quality (e.g. water holding capacity). Four spectroscopic techniques, Raman, Fourier transform infrared (FT-IR), near infrared (NIR) and fluorescence spectroscopy, were used to study protein and amino acid modifications to determine pH-related changes in pork myofibril extracts at three different pH-levels, 5.3, 5.8 and 6.3. Protonation of side-chain carboxylic acids of aspartic and glutamic acid and changes in secondary structure, mainly the amide I-III peaks, were the most important features identified by Raman and FT-IR spectroscopy linked to changes in pH. Fluorescence spectroscopy identified tryptophan interaction with the molecular environment as the most important contributor to changes in the spectra. NIR spectroscopy gave no significant contributions to interpreting protein structure related to pH. Results from our study are useful for interpreting spectroscopic data from meat where pH is an important variable.