Inter seasonal validation of non-contact NIR spectroscopy for measurement of total soluble solids in high tunnel strawberries

Autonomous field robots are being developed for picking of fruit, where each fruit needs to be individually graded and handled. There is therefore a need for rapid and non-destructive sensing to measure critical fruit quality parameters. In this article we report how total soluble solids (TSS), a measure for total sugar content, can be measured in strawberries in the field by non-contact near-infrared (NIR) interaction spectroscopy. A specially designed prototype system working in the wavelength range 760-1080 nm was tested for this purpose. This novel instrument was compared with a commercial handheld NIR reflection instrument working in the range 900-1600 nm. The instruments were calibrated in the lab using data collected from 200 strawberries of two varieties and tested in a strawberry field on 50 berries in 2022 and 100 berries in 2023. Both systems performed well during calibration with root mean square errors of cross validation for TSS around 0.49 % and 0.57 %, for interaction and reflection, respectively. For prediction of TSS in new berries in 2023, the interaction system was superior, with a prediction error of 1.0 % versus 8.1 % for the reflection system, most likely because interaction probes deeper into the berries. The results suggest that interaction measurements of average TSS are more robust and would most likely require less calibration maintenance compared to reflection measurements. The non-contact feature is important since it reduces the spread of diseases and physical damage to the berries.

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 Raman spectroscopy for characterization of an industrial poultry raw material stream

In this work, we evaluated the feasibility of Raman spectroscopy as an in-line raw material characterization tool for industrial process control of the hydrolysis of poultry rest raw material. We established calibrations (N = 59) for fat, protein, ash (proxy for bone) and hydroxyproline (proxy for collagen) in ground poultry rest raw material. Calibrations were established in the laboratory using poultry samples with high compositional variation. Samples were measured using a wide area illumination Raman probe at varying working distance (6 cm, 9 cm, 12 cm) and probe tilt angle (0°, 30°) to mimic expected in-line variations in the measurement situation. These moderate variations did not significantly affect performance for any analytes. The obtained calibrations were tested in-line with continuous measurements of the ground poultry by-product stream at a commercial hydrolysis facility over the course of two days. Measurements were acquired under demanding conditions, e.g. large variations in working distance. Reasonable estimates of compositional trends were obtained. Validation samples (N = 19) were also reasonably well predicted, with RMSEPcorr = [0.14, 1.37, 2.36, 1.51]% for hydroxyproline, protein, fat and ash, respectively. However, there were indications that further calibration development and robustification of pre-processing would be advantageous, particularly with respect to hydroxyproline and protein models. It is the authors' impression that with such efforts, potentially in combination with development of practical measurement setup, the use of Raman spectroscopy as a process control tool for the hydrolysis of poultry rest raw materials is within reach.

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.

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 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.

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.

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.

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 (R²_CV) 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 (R²_CV 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.

NIR Spectroscopic Techniques for Quality and Process Control in the Meat Industry

Near-infrared spectroscopy (NIRS) is widely used for quality and process control in the food industry. In the meat industry, the method is still used mainly for determination of fat, water, and protein content, while new applications are emerging. In this paper, we report on how in-line NIRS can be used to detect and sort chicken breast fillets with the myopathies wooden breast and spaghetti meat from normal fillets. A total of 270 fillets were measured with 2 different near-infrared systems. The near-infrared spectra contained information about protein content and water-binding properties that showed systematic differences between the 3 quality classes. Wooden breast could be well separated fro m normal fillets (96% correct classification), while spaghetti meat was slightly more difficult to separate from both normal and wooden breast because properties measured by NIRS were overlapping between the 3 groups. Two quite similar NIRS instruments had quite different classification performance, which emphasizes the importance of optimizing spectroscopic instrumentation for different applications.

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.

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 β-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.

Rapid on-line detection and grading of wooden breast myopathy in chicken fillets by near-infrared spectroscopy

The main objective of this work was to develop a method for rapid and non-destructive detection and grading of wooden breast (WB) syndrome in chicken breast fillets. Near-infrared (NIR) spectroscopy was chosen as detection method, and an industrial NIR scanner was applied and tested for large scale on-line detection of the syndrome. Two approaches were evaluated for discrimination of WB fillets: 1) Linear discriminant analysis based on NIR spectra only, and 2) a regression model for protein was made based on NIR spectra and the estimated concentrations of protein were used for discrimination. A sample set of 197 fillets was used for training and calibration. A test set was recorded under industrial conditions and contained spectra from 79 fillets. The classification methods obtained 99.5-100% correct classification of the calibration set and 100% correct classification of the test set. The NIR scanner was then installed in a commercial chicken processing plant and could detect incidence rates of WB in large batches of fillets. Examples of incidence are shown for three broiler flocks where a high number of fillets (9063, 6330 and 10483) were effectively measured. Prevalence of WB of 0.1%, 6.6% and 8.5% were estimated for these flocks based on the complete sample volumes. Such an on-line system can be used to alleviate the challenges WB represents to the poultry meat industry. It enables automatic quality sorting of chicken fillets to different product categories. Manual laborious grading can be avoided. Incidences of WB from different farms and flocks can be tracked and information can be used to understand and point out main causes for WB in the chicken production. This knowledge can be used to improve the production procedures and reduce today's extensive occurrence of WB.

Rapid and Nondestructive Determination of Aleurone Content in Pearling Fractions of Barley by Near-Infrared (NIR) and Fluorescence Spectroscopies

The aim of the present work was to develop and evaluate near-infrared (NIR) and fluorescence spectroscopies as rapid and potential online methods for determination of the amount of aleurone in pearling dust fractions of barley. Phytic acid was used as a marker for the aleurone layer. Different varieties of barley were pearled, and dust fractions were progressively taken out. Sample concentration of phytic acid varied in the range of 0.5-4.1 g/100 g, and highest concentrations were obtained in dust fractions for pearling degrees in the range of 15-25%. Regression models for phytic acid were developed with the same high correlations for NIR and fluorescence spectroscopies (R² = 0.96) and prediction errors of ±0.16-0.18 g/100 g. The models performed well on a test set of pearling fractions from two other barley varieties. The techniques are rapid and nondestructive, which means that they can be used online in connection with industrial pearling systems.

Chemical Imaging of Heterogeneous Muscle Foods Using Near-Infrared Hyperspectral Imaging in Transmission Mode

Foods and biomaterials are, in general, heterogeneous and it is often a challenge to obtain spectral data which are representative for the chemical composition and distribution. This paper presents a setup for near-infrared (NIR) transmission imaging where the samples are completely trans-illuminated, probing the entire sample. The system measures falling samples at high speed and consists of an NIR imaging scanner covering the spectral range 760-1040 nm and a powerful line light source. The investigated samples were rather big: whole pork bellies of thickness up to 5 cm, salmon fillets with skin, and 3 cm thick model samples of ground pork meat. Partial least square regression models for fat were developed for ground pork and salmon fillet with high correlations (R = 0.98 and R = 0.95, respectively). The regression models were applied at pixel level in the hyperspectral transmission images and resulted in images of fat distribution where also deeply embedded fat clearly contributed to the result. The results suggest that it is possible to use transmission imaging for rapid, nondestructive, and representative sampling of very heterogeneous foods. The proposed system is suitable for industrial use.

A novel approach for subsurface through-skin analysis of salmon using spatially offset Raman spectroscopy (SORS)

In the present study, the possibility of employing spatially offset Raman spectroscopy (SORS) in the qualitative and quantitative characterization of quality parameters of salmon through the skin has been explored. A laboratory-based SORS setup comprising an 830 nm laser was employed, and intact samples and model samples made of salmon tissue constituents were used to investigate the penetration of Raman signals through the dark and light part of salmon skin. Intact salmon samples with both dark and light skin were measured at different spatial offsets. When using spatial offsets in the range of 5-6 mm, the results clearly show that information regarding fatty acid composition and carotenoid content could be obtained from both dark and light parts of the skin. Similar information could not be obtained using conventional backscattering Raman spectroscopy. Model samples of ground salmon spiked with either solutions of carotenoids or a range of vegetable oils were also measured, and at a spatial offset of 5 mm, a clear relationship between Raman carotenoid band intensities and carotenoid concentrations in the model samples was revealed. In addition, high correlations for the estimation of iodine values (i.e., fatty acid unsaturation) could be obtained for SORS measurements through light and dark parts of the salmon skin. A crude estimate suggested that information from around 5 mm beneath the surface area of the salmon skin could be obtained. The choice of a laser line in the near-infrared region is a major prerequisite for successful through-skin analysis of salmon. This feasibility study could pave the way for future Raman analysis of intact salmon.

Effects of storage atmosphere and heme state on the color and visible reflectance spectra of salmon ( Salmo salar ) fillets

It has previously been observed that the color of mackerel muscle is dependent on the status of heme as myoglobin and hemoglobin and hence the storage atmosphere. This study gives strong indications of this being the case also in salmon. Three different storage conditions were used to promote the oxidized, reduced, and carbon monoxide (CO) bound forms of heme in salmon and mackerel fillets. Color determination (instrumental color analysis, imaging, and sensory evaluation) and spectroscopic measurements were performed to study how spectral changes corresponded to color variations. Storage in CO significantly increased the redness in mackerel. This was also seen in salmon to such a degree that it was visible over normal levels of salmon carotenoids. Air storage increased the yellowness and reduced the redness in mackerel, but this effect was partly concealed in salmon by the astaxanthin absorption. The spectral differences due to storage condition could be ascribed to the spectral features characterizing heme of different oxidation states and bound to different ligands. The status of heme should therefore always be considered when experiments related to salmon color are performed. The findings could help in the understanding, control, and prediction of color loss in salmon during processing, storage, and transport.

Effect of naturally occurring tetrapyrroles on photooxidation in cow's milk

The objective of this work was to better understand the photosensitizing effect of riboflavin versus naturally occurring tetrapyrroles in cow's milk. This was done by exposure of milk samples to blue light (400-500 nm), which is absorbed by riboflavin and tetrapyrroles, orange light (575-750 nm), which is absorbed by tetrapyrroles but not riboflavin, and white light, which contains the entire visible region. The milk was exposed to about 1.6 W/m(2) in 20 h, and two different light sources were tested: HMI lamp and fluorescent light tubes used for commercial display. Sensory analysis showed that wavelengths longer than 575 nm induced significantly more off-flavors than wavelengths shorter than 500 nm. By fluorescence spectroscopy it was observed that tetrapyrroles, in particular, chlorophyllic compounds, were degraded more by orange light than by blue and that the degree of degradation correlated closely with the formation of sensory off-flavors. The fluorescent agent Singlet Oxygen Sensor Green (SOSG) was used to monitor the formation of singlet oxygen under the different light exposure conditions, and the method verified that singlet oxygen was formed in large proportions in milk exposed to wavelengths longer than 575 nm, presumably with minor or no involvement of riboflavin. The results suggest that cholorophyllic compounds are responsible for a major part of photooxidation in milk. It is also suggested that β-carotene protects against photooxidation under blue light because it absorbs a major portion of the light below 500 nm and thereby reduces reactions with photosensitizers.

Scattering correction by use of a priori information

In this paper we demonstrate how a limited amount of a priori knowledge about spectral variability can be used in extended multiplicative scattering correction (EMSC) to remove disturbing effects such as light scattering variation in visible and near-infrared spectra prior to data modeling. Two different datasets were studied. In the first dataset, pigment concentrations (astaxanthin) were estimated in a model system with different concentrations of the scattering agent intralipid. Different cases were created by including varying levels of intralipid in the calibration set and then applying the models on sample sets with scattering properties both within and outside the calibration range. Including the most accurate estimate of light scattering in the EMSC model gave root mean square errors of prediction (RMSEP) that were similar to a cross-validated global model including all samples, even for extreme extrapolation with regard to scattering properties. Less accurate estimates gave on average RMSEPs half of what could be achieved using EMSC without any a priori knowledge, suggesting that the method also has potential in cases where the accurate light scattering spectrum is difficult to obtain. In the second dataset carbohydrate concentrations (sucrose, fructose, and glucose) were estimated in orange-juice mixtures where unwanted spectral variation was caused by a change in distance between transmittance fiber-optic probes. This caused two different interfering phenomena due to path length variation and saturation in the detection system. The prediction results for a model based on spectra collected at one specific probe distance treated with EMSC with a correction spectrum were comparable to what could be achieved by a global model including spectra collected at three different distances. The corresponding RMSEPs for models using EMSC with no correction term were in the worst cases 4, 22, and 36 times higher for sucrose, fructose, and glucose, respectively.

Rapid nondestructive determination of edible meat content in crabs (Cancer pagurus) by near-infrared imaging spectroscopy

This article presents a method by which noncontact near-infrared (NIR) interactance imaging spectroscopy can be applied to determine the amount of edible meat in single live crabs (Cancer pagurus) on a conveyor belt at high speed. The physiology and optical properties of the crabs are presented and discussed in order to explain the requirements for representative spectroscopic sampling. Two different sampling and calibration strategies are discussed. One strategy is based on the extraction of one average NIR spectrum from certain locations in each crab. The other strategy relies on first making a model based on average spectra from a certain location, and then using this model for pixel-wise prediction of the meat content within the crabs. A measure of the predicted distribution of meat is then used for calibration. Reference measurements of meat content were based on an objective quantitative metric of the meat content. The results show that NIR imaging enables on-line grading of the crabs with a correlation of 0.96 with the measured meat content. Due to seasonal variations in the crabs, a piece-wise regression strategy performs slightly better than a global model. Pixel-wise predictions of meat content provide informative images showing the distribution and amount of meat within each crab.

Assessment of the quality attributes of cod caviar paste by means of front-face fluorescence spectroscopy

This paper describes the fluorescent behavior of cod caviar paste, stored under different conditions, in terms of light exposure and concentration of oxygen in the headspace. Multivariate curve resolution was employed to decompose the overall fluorescence spectra into pure fluorescent components and calculate the relative concentrations of these components in the different samples. Profiles corresponding to protoporphyrin IX, photoprotoporphyrin, and fluorescent oxidation products were identified. Sensory evaluation, TBARS, and analysis of volatiles are typical methods employed in the routine analysis and quality control of such food. Successful calibration models were established between fluorescence and those routine methods. Correlation coefficients higher than 0.80 were found for 79% and higher than 0.90 for 50% of the assessed odors and flavors. For instance, R values of 0.94, and 0.96 were obtained for fresh and rancid flavors respectively, and 0.89 for TBARS. On the basis of these data, it can be argued that front-face fluorescence spectroscopy can substitute all of these expensive and tedious methodologies.

Usefulness of fluorescence excitation-emission matrices in combination with PARAFAC, as fingerprints of red wines

The possibility of using front-face fluorescence spectroscopy to characterize red wines was investigated, and a tentative identification of their main fluorescent components was attempted. Fifty-seven red wine samples from different origins were included in the present study. Their fluorescence excitation-emission matrices (EEMs) were registered directly on 3-mL aliquots of untreated samples. The assayed excitation and emission ranges were 245-340 and 300-500 nm, respectively. The set of 57 EEMs was analyzed by means of parallel factor analysis (PARAFAC). Thus, the spectral excitation and emission profiles of possible "pure" fluorescence components (PARAFAC loadings) and the relative contribution of each component to the individual EEMs (PARAFAC scores) were obtained. The red wine system contained four main fluorescence components, and the excitation and emission loadings had maxima at the wavelength pairs 260/380, 275/323, 330/410, and 280/364 nm, respectively. A tentative identification of fluorophores was done by matching PARAFAC score values with HPLC measurements on the same 57 samples, as well as fluorescence measurements on pure compounds typically present in red wine. It was found that the third component was highly correlated with concentrations of catechin and epicatechin. When the PARAFAC score values were plotted against each other, they did to some extent discriminate the wines according to origin (country) and grape variety.

Noncontact salt and fat distributional analysis in salted and smoked salmon fillets using X-ray computed tomography and NIR interactance imaging

To be able to monitor the salting process of cold smoked salmon, a nondestructive imaging technique for salt analysis is required. This experiment showed that X-ray computed tomography (CT) can be used for nondestructive distributional analysis of NaCl in salmon fillets during salting, salt equilibration, and smoking. The combination of three X-ray voltages (80, 110, and 130 kV) gave the best CT calibrations for NaCl, with a prediction error (root mean square error of cross-validation, RMSECV) of 0.40% NaCl and a correlation (R) of 0.92 between predicted values and reference values. Adding fat predictions based on NIR interactance imaging further improved the NaCl prediction performance, giving RMSECV = 0.34% NaCl and R = 0.95. It was also found that NIR interactance imaging alone was able to predict NaCl contents locally in salted salmon fillets with RMSECV = 0.56% and R = 0.86.

High-speed assessment of fat and water content distribution in fish fillets using online imaging spectroscopy

A nondestructive method using online spectral imaging has been developed for quantitative measurements of moisture and fat distribution in six species of fish fillets: Atlantic halibut (Hippoglossus hippoglossus), catfish (Icatalurus punctatus), cod (Gadus morhua), mackerel (Scomber japonicus), herring (Clupea harengus), and saithe (Pollachius virens). A spectral image cube was acquired for each fish fillet, and a subsampling approach for relating spectral and chemical features was applied. Spectral data was first analyzed by partial least-squares regression (PLSR), and then the regression coefficients were applied pixel-wise to convert the pixel spectra to a meaningful distribution map of moisture and fat contents. The resulting images are called "chemical images", which illustrate the distribution of fat and/or water content in the fillets. The pixel-wise prediction models for water and fat content had a correlation value of 0.94 with root-mean-square error estimated by a cross-validation (RMSECV) of 2.73% and a correlation value of 0.91 with RMSECV of 2.99%, respectively. This technique is suitable for high-speed assessment of quality parameters of biomaterials and should thus be implemented in industrial applications. The product could comprehensively be defined not only in terms of its external features such as size, shape, and color but also in terms of its chemical composition and its spatial distribution.

An improved pixel-based approach for analyzing images in two-dimensional gel electrophoresis

An improved pixel-based approach for analyzing 2-DE images is presented. The key feature of the method is to create a mask based on all gels in the experiment using image morphology, followed by multivariate analysis on the pixel level. The method reduces the impact of noise and background by identifying regions in the image where protein spots are present, but make no assumption on individual spot boundaries for isolated spots. This makes it possible to detect significant changes in complex regions, and visualize these changes over multiple gels in an easy way. False missing values and spot volumes caused by imposing erroneous spot boundaries are thus circumvented. The approach presented gives improved pixel-based information from the gels, and is also an alternative to existing methods for data-reduction, significance testing and visualization of 2-DE data. Results are compared with software using a common spot boundary approach on an experiment consisting of 35 full size gel images. Gel alignment is required before analysis.

Pixel-based analysis of multiple images for the identification of changes: a novel approach applied to unravel proteome patterns [corrected] of 2-D electrophoresis gel images

A novel approach for revealing patterns of proteome variation among series of 2-DE gel images is presented. The approach utilises image alignment to ensure that each pixel represents the same information across all gels. Gel images are normalised, and background corrected, followed by unfolding of the images to 1-D pixel vectors and analysing pixel vectors by multivariate data modelling. Information resulting from the data analysis is refolded back to the image domain for visualisation and interpretation. The method is rapid and suitable for automatic routines applied after the gel alignment. The approach is compared with spot volume analysis to illustrate how this approach can solve persistent problems like mismatch of protein spots, erroneous missing values and failure to detect variation in overlapping proteins. The method may also detect variation in the border area of saturated proteins. The approach is given the name pixel-based analysis of multiple images for the identification of changes (PMC). The method can be used for multiple images in general. Effects of pretreatment of the images are discussed.

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.

Front-Face Fluorescence Measurement of Photosensitizers and Lipid Oxidation Products During the Photooxidation of Butter

This paper shows that fluorescence spectroscopy can measure both degradation of photosensitizers and formation of lipid oxidation products in light-exposed butter. The photosensitizers were already notably degraded after 4 h of light exposure, whereas fluorescent lipid oxidation products were detected after 5 d. The fluorescence measurements were highly correlated with sensory assessments of acidic and rancid flavor. Photosensitizer degradation is therefore a promising indirect indicator of the onset of lipid oxidation in butter. Sensory analysis and measurement of peroxide value showed that the level of lipid oxidation was significantly higher for butter stored in air compared with butter stored in nitrogen (N2). This might be explained by the formation of singlet oxygen from direct photooxidation and type II photosensitized oxidation. Addition of the singlet oxygen quencher beta-carotene reduced the rancid flavor intensity in the air and N2 packages from 9.0 to 4.9 and from 6.5 to 4.7, respectively. Results indicate that lipid oxidation in the butter stored in N2 was mainly caused by type I photosensitized reactions, because addition of beta-carotene had little effect on the rancid flavor intensity.

Active photosensitizers in butter detected by fluorescence spectroscopy and multivariate curve resolution

In this study, fluorescence excitation and emission matrices and multivariate curve resolution (PARAFAC) were used to detect and characterize active photosensitizers spectrally in butter. Butter samples were packed under high (air) and low oxygen (<0.05%) atmospheres and exposed to violet, green, or red light. Six photosensitizers were found: riboflavin, protoporphyrin, hematoporphyrin, a chlorophyll a-like molecule, and two unidentified tetrapyrrols. By estimation of relative concentrations, we could follow how each sensitizer was photodegraded as function of wavelength, oxygen level, and time. The degradation rate of protoporphyrin, hematoporphyrin, chlorophyll a, and one of the tetrapyrrols correlated well (0.83-0.91) with the formation of sensory measured oxidation. The results suggest that mainly type I photoreactions were responsible for the degradation of photosensitizers in both high and low oxygen atmosphere. Type II photoreactions (generation of singlet oxygen) were involved in the oxidation of butter stored in air. The study shows that PARAFAC modeling of fluorescence landscapes is an excellent tool for studying photooxidation in complex systems.

Raman spectra of biological samples: A study of preprocessing methods

In this study preprocessing of Raman spectra of different biological samples has been studied, and their effect on the ability to extract robust and quantitative information has been evaluated. Four data sets of Raman spectra were chosen in order to cover different aspects of biological Raman spectra, and the samples constituted salmon oils, juice samples, salmon meat, and mixtures of fat, protein, and water. A range of frequently used preprocessing methods, as well as combinations of different methods, was evaluated. Different aspects of regression results obtained from partial least squares regression (PLSR) were used as indicators for comparing the effect of different preprocessing methods. The results, as expected, suggest that baseline correction methods should be performed in advance of normalization methods. By performing total intensity normalization after adequate baseline correction, robust calibration models were obtained for all data sets. Combination methods like standard normal variate (SNV), multiplicative signal correction (MSC), and extended multiplicative signal correction (EMSC) in their basic form were not able to handle the baseline features present in several of the data sets, and these methods thus provide no additional benefits compared to the approach of baseline correction in advance of total intensity normalization. EMSC provides additional possibilities that require further investigation.

The potential of Raman spectroscopy for characterisation of the fatty acid unsaturation of salmon

Raman spectroscopy has been evaluated for characterisation of the degree of fatty acid unsaturation (iodine value) of salmon (Salmo salar). The Norwegian Quality Cuts from 50 salmon samples were obtained, and the samples provided an iodine value range of 147.8-170.0 g I2/100 g fat, reflecting a normal variation of farmed salmon. Raman measurements were performed both on different spots of the intact salmon muscle, on ground salmon samples as well as on oil extracts, and partial least squares regression (PLSR) was utilised for calibration. The oil spectra provided better iodine value predictions than the other data sets, and a correlation coefficient of 0.87 with a root mean square error of cross-validation of 2.5 g I2/100 g fat was achieved using only one PLSR component. The ground samples provided comparable results, but at least two PLSR components were needed. Higher prediction errors were obtained from Raman spectra of intact salmon muscle, and this may partly be explained by sampling uncertainties in the relation between Raman measurements and reference analysis. All PLSR models obtained were based on chemically sound regression coefficients, and thus information regarding fatty acid unsaturation is readily available from Raman spectra even in systems with high contents of protein and water. The accuracy, the robustness and the low complexity of the PLSR models obtained suggest Raman spectroscopy as a promising method for rapid in-process control of the degree of unsaturation in salmon samples.

Raman and near-infrared spectroscopy for quantification of fat composition in a complex food model system

Raman and near-infrared (NIR) spectroscopy have been evaluated for determining fatty acid composition and contents of main constituents in a complex food model system. A model system consisting of 70 different mixtures of protein, water, and oil blends was developed in order to create a rough chemical imitation of typical fish and meat samples, showing variation both in fatty acid composition and in contents of main constituents. The model samples as well as the pure oil mixtures were measured using Raman and NIR techniques. Partial least squares regression was utilized for prediction, and fatty acid features were expressed in terms of the iodine value and as contents of saturated, monounsaturated, and polyunsaturated fatty acids. Raman spectroscopy provided the best results for predicting iodine values of the model samples, giving validated estimation errors accounting for 2.8% of the total iodine value range. Both techniques provided good results for predicting the content of saturated, monounsaturated, and polyunsaturated fatty acids in the model samples, yielding validated estimation errors in the range of 2.4-6.1% of the total range of fatty acid content. Prediction results for determining fatty acid features of the pure oil mixtures were similar for the two techniques. NIR was clearly the best technique for modeling content of main constituents in the model samples.

Rapid quantification of carotenoids and fat in Atlantic Salmon (Salmo salar L.) by Raman spectroscopy and chemometrics

Raman spectroscopy (785 nm excitation) was used to determine the overall carotenoid (astaxanthin and cantaxanthin) and fat content in 49 samples of ground muscle tissue from farmed Atlantic salmon (Salmo salar L.). Chemically determined contents ranged from 1.0 to 6.8 mg/kg carotenoids and 36 to 205 g/kg fat. In addition to the raw Raman spectra, three types of spectral preprocessing were evaluated: the first derivative, subtraction of the fitted fourth-order polynomial (POLY), and the intensity normalized versions of POLY (POLY-SNV). Further, variable selection based on significance testing by use of jack-knifing was performed on each spectral data set. Partial least-squares regression resulted in a root mean square error of prediction of 0.33 mg/kg (R = 0.97) for carotenoids for the variable selected versions of all the preprocessed spectral data sets. The fat content was best estimated by the variable selected POLYSNV, resulting in a root mean square error of prediction of 15.5 g/kg (R = 0.95). Both preprocessing and variable selection improved the regression models significantly. The results demonstrate that Raman spectroscopy is a suitable method for simultaneous, rapid, and nondestructive quantification of both pigments and fat in ground salmon muscle tissue.