Prediction of the chemical composition of freeze dried ostrich meat with near infrared reflectance spectroscopy

The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades

During food quality control, NIR technology enables the rapid and non-destructive determination of the typical quality characteristics of food categories, their origin, and the detection of potential counterfeits. Over the past 20 years, the NIR results for a variety of food groups-including meat and meat products, milk and milk products, baked goods, pasta, honey, vegetables, fruits, and luxury items like coffee, tea, and chocolate-have been compiled. This review aims to give a broad overview of the NIRS processes that have been used thus far to assist researchers employing non-destructive techniques in comparing their findings with earlier data and determining new research directions.

Development and validation of a near-infrared spectroscopy model for the prediction of muscle protein in Chinese native chickens

This study investigated the ability of the near-infrared spectroscopy (NIRS) model to predict the protein of freeze-dried muscle samples in Chinese native chickens and to determine the accuracy of the models for other native chicken breeds. Spectral pretreatment, wavelength selection, and outlier sample elimination were used to optimize the calibration models. The results showed that the best model was obtained by using a combination of standard normal variable transformation and gap-segment first-derivative pretreatment spectra after removing 48 outliers in the wavelength range of 1,439 to 1,900 nm, with coefficient of determination for the calibration (R2C) of 0.95, standard error of cross-validation (SECV) of 1.18, coefficient of determination for the prediction (R2P) of 0.95, the ratio of the standard deviation of the validation to the standard deviation of the calibration (RPDP) of 4.62. The findings indicated that NIRS can be used to predict the protein of freeze-dried muscle in Chinese native chickens.

Objective grading of rib eye traits using the Q-FOM™ camera in Australian beef carcasses

The objective of this study was to develop calibration models against rib eye traits and independently validate the precision, accuracy, and repeatability of the Frontmatec Q-FOM™ Beef grading camera in Australian carcasses. This study compiled 12 different research datasets acquired from commercial processing facilities and were comprised of a diverse range of carcass phenotypes, graded by industry identified expert Meat Standards Australia (MSA) graders and sampled for chemical intramuscular fat (IMF%). Calibration performance was maintained when the device was independently validated. For continuous traits, the Q-FOM™ demonstrated precise (root mean squared error of prediction, RMSEP) and accurate (coefficient of determination, R2) prediction of eye muscle area (EMA) (R2 = 0.89, RMSEP = 4.3 cm2, slope = 0.96, bias = 0.7), MSA marbling (R2 = 0.95, RMSEP = 47.2, slope = 0.98, bias = -12.8) and chemical IMF% (R2 = 0.94, RMSEP = 1.56%, slope = 0.96, bias = 0.64). For categorical traits, the Q-FOM™ predicted 61%, 64.3% and 60.8% of AUS-MEAT marbling, meat colour and fat colour scores equivalent, and 95% within ±1 classes of expert grader scores. The Q-FOM™ also demonstrated very high repeatability and reproducibility across all traits.

Characterisation and Identification of Individual Intact Goat Muscle Samples (Capra sp.) Using a Portable Near-Infrared Spectrometer and Chemometrics

Adulterated, poor-quality, and unsafe foods, including meat, are still major issues for both the food industry and consumers, which have driven efforts to find alternative technologies to detect these challenges. This study evaluated the use of a portable near-infrared (NIR) instrument, combined with chemometrics, to identify and classify individual-intact fresh goat muscle samples. Fresh goat carcasses (n = 35; 19 to 21.7 Kg LW) from different animals (age, breeds, sex) were used and separated into different commercial cuts. Thus, the longissimus thoracis et lumborum, biceps femoris, semimembranosus, semitendinosus, supraspinatus, and infraspinatus muscles were removed and scanned (900–1600 nm) using a portable NIR instrument. Differences in the NIR spectra of the muscles were observed at wavelengths of around 976 nm, 1180 nm, and 1430 nm, associated with water and fat content (e.g., intramuscular fat). The classification of individual muscle samples was achieved by linear discriminant analysis (LDA) with acceptable accuracies (68–94%) using the second-derivative NIR spectra. The results indicated that NIR spectroscopy could be used to identify individual goat muscles.

Determination of Moisture and Protein Content in Living Mealworm Larvae (Tenebrio molitor L.) Using Near-Infrared Reflectance Spectroscopy (NIRS)

Yellow mealworm larvae (Tenebrio molitor L.) are a sustainable source of protein for food and feed. This study represents a new approach in analyzing changes in the nutritional composition of mealworm larvae using near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis. The moisture and protein content of living larvae were scanned with a near-infrared spectrometer using wavelengths from 1100 to 2100 nm. Different feeding groups with varying moisture sources and amount and the difference between low (50%) and high (75%) humidity were tested, and the influence on larval moisture and protein content was measured. A calibration was developed, with modified partial least squares as the regression method. The NIR spectra were influenced by the moisture and protein content of the larvae, because the absorbance values of the larval groups differed greatly. The coefficient of the determination of calibration (R2c) and prediction (R2p) were over 0.98 for moisture and over 0.94 for protein content. The moisture source and content also had a significant influence on the weight gain of the larvae. Consequently, significant differences in protein content could be determined, depending on the water supply available. With respect to wet weight, the larvae moisture content varied from 60 to 74% and protein content from 16 to 24%. This investigation revealed that with non-invasive NIRS online monitoring, the composition of insects can be continuously recorded and evaluated so that specific feeding can be carried out in the course of larval development and composition.

An Alternative Approach to Evaluate the Quality of Protein-Based Raw Materials for Dry Pet Food

The majority of dry pet food currently on the market is produced using fresh meats (FMs) and especially meat meals (MMs) as the main protein source. The transport and storage conditions of the raw materials, together with thermal and mechanical treatments in the case of MMs, may result in undesirable alterations of food products and their protein content. This study was conducted to analyze the protein component of three different kinds of raw materials used for dry pet food production, i.e., chicken, pork, and salmon. The quantitative analysis of the protein component was determined using the traditional Kjeldahl method and near-infrared (NIR) spectroscopy, and an alternative method, i.e., the Bradford assay, while the qualitative analysis was performed through SDS-PAGE, followed by Coomassie Blue staining. The amino acid (AA) profile was also evaluated by quadrupole time-of-flight liquid chromatography/mass spectrometry (Q-TOF LC/MS). In addition, the digestibility was tested through in vitro gastric and small intestine digestion simulation. Statistical analysis was performed by the Student's t-test, and data are reported as mean ± SEM, n = 10 (p < 0.05). The results showed that the MMs are lower in quality compared to FMs, both in terms of protein bioavailability and digestibility, having a lower soluble protein (SP) content (chicken MM = 8.6 g SP/100 g dry sample; pork MM = 6.2 g SP/100 g dry sample; salmon MM = 7.9 g SP/100 g dry sample) compared to FMs (chicken FM = 14.6 g SP/100 g dry sample; pork FM = 15.1 g SP/100 g dry sample; salmon FM = 13.7 g SP/100 g dry sample). FMs appear, therefore, to be higher-quality ingredients for pet food production. Moreover, the Bradford assay proved to be a quick and simple method to better estimate protein bioavailability in the raw materials used for dry pet food production, thanks to its correlation with the in vitro digestibility.

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue-Critical Overview

Amid today's stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

Chemical Composition of Edible Ostrich Offal

The offal (hearts, stomachs, and livers) of 24 African ostriches (Strutio camelus var. domesticus) from Polish farms were used in this study. Offal were taken directly from the production line; they were weighed and their water, fat, protein, ash and total collagen contents were determined. Ostrich hearts and stomachs were found to have high protein (18.1% and 19.0%, respectively) and low fat content (2.0% and 0.9%, respectively), typical of lean meat. Thus, the offal could be used in processed offal products or in pet food. Ostrich livers had slightly lower protein content (16.6%) and significantly higher and diverse fat content (4.4–28.4%). Heavier livers had significantly (P<0.05) higher fat and lower protein, water, and ash content. The utilization of ostrich liver should be preceded by classification of its fat content.

Carbonic anhydrase inhibitors. Inhibition of red blood cell ostrich (Struthio camelus) carbonic anhydrase with a series of aromatic and heterocyclic sulfonamides

The purification of red blood cell carbonic anhydrase (CA, EC 4.2.1.1) from ostrich (scCA) blood is reported, as well as an inhibition study of this enzyme with a series of aromatic and heterocylic sulfonamides. The ostrich enzyme showed a high activity, comparable to that of the human isozyme II, with kcat, of 1.2 x 10(6) s(-1) and kcat/KM of 1.8 x 10(7) M(-1)s(-1), and an inhibition profile quite different from that of the human red blood cell cytosolic isozymes hCA I and II. scCA has generally a lower affinity for sulfonamide inhibitors as compared to hCA I and II. The only sulfonamide which behaved as a very potent inhibitor of this enzyme was ethoxzolamide (KI = 3.9 nM) whereas acetazolamide and sulfanilamide behaved as weaker inhibitors (inhibition constants in the range 303-570 nM). Several other aromatic and heterocyclic sulfonamides, mostly derivatives of sulfanilamide, homosulfanilamide, 4-aminoethylbenzenesulfonamide or 5-amino-1,3,4-thiadiazole-2-sulfonamide, showed good affinities for the ostrich enzyme, with KI values in the range 25-72 nM.