Identification of turkey meat and processed products using near infrared spectroscopy

Barbin, Douglas Fernandes; Badaró, Amanda Teixeira; Honorato, Danielle C. B.; Ida, Elza Youko; Shimokomaki, Massami

doi:10.1016/j.foodcont.2019.106816

Cited by 45 publications

(24 citation statements)

References 21 publications

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“…NIFEXT fraction calculation presented a value of 0.88% (dashes) for the carbohydrate present in the turkey neck. This value matches carbohydrate contents found for the Turkey leg (0.81%) by Barbin et al (2020) [1].…”

Section: Chemical Composition Of Turkey Neck Meatsupporting

confidence: 88%

“…Turkey neck meat presented moisture content below other meat products (69.41 g ± 0.33 g of water/100 g sample, on wet basis). This value is little that moisture for other turkey cuts, such as leg (74.87% and 75.04%) and breast (73.23% and 74.9%) [1,22]. Lipid content found was 6.45 ± 0.24%, superior to the chicken neck (3.60%) [23] and close to the found by Torres et al (2000) [22] for Turkey leg cut (7.43%).…”

Section: Chemical Composition Of Turkey Neck Meatsupporting

confidence: 81%

“…Protein value was 22.29% ± 0.3%, close to the protein content of the Turkey wing (23.98%), and the Turkey breast (24.80%), which suggests that this cut is an excellent protein source. Ash content found was 0.97 ± 0.02%, within the range found by Torres et al (2000) [22] for the Turkey breast and leg (1.12% and 0.93%, respectively) and by Barbin et al (2020) [1] for both Turkey breast and leg (1.13%). Zapata et al (1998) [24] describe that darker cuts (chicken leg) are more abundant in calcium, iron, sodium, and zinc, and weaker in phosphorus, magnesium, and potassium when compared to light cuts (breast).…”

Section: Chemical Composition Of Turkey Neck Meatsupporting

confidence: 76%

“…Turkey meat consumption has been linked to its nutritional value because its low cuts (breast, wing, thigh, and drumstick) have high protein content (between 19.42% until 20.84%), low lipid content (1.5% until 3.77%) and low calorie, what contributes with a healthy diet [1,2]. Among the turkey cuts, it is observed that the neck is little valued.…”

Section: Introductionmentioning

confidence: 99%

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Mass transfer and physicochemical characteristics of turkey neck meat during dry salting

et al. 2020

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The turkey neck is a cut with a higher proportion of meat when compared to the neck of other poultry, abundant in red fibers and is little valued by the food industry. It is possible to add value to this by-product through dry salting, an inexpensive and suitable method for the production of dried meat products, such as charqui. Turkey neck meat was submitted to the dry salting under different temperatures for study their salt gain and water loss on the flat plate geometry, using derived equations of Fick's Law, beyond the influence of the process in the physicochemical characteristics. The highest water loss (33.99%) occurred at 10 °C, while the highest salt gain (9.47%) was observed at 15 °C. The empirical model presented a good fit to the experimental data. Apparent diffusivities were between 1.02 x 10 -10 m 2 /s and 1.18 x 10 -10 m 2 /s. The dry salting promoted small decrease in pH, the darkening of the meat, while shear force increased. After the process, water activity (aw) was 0.74 and 0.79, moisture between 40.14% and 45.97%, and ash residue (12.30% -14.03%), which characterizes a salt product with similar characteristics of charqui meat. It is possible to estimate the desirable amount of salt, producing a stable food product with a high conservation potential and a wide range of applications for derived salty products.

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Section: Chemical Composition Of Turkey Neck Meatsupporting

confidence: 88%

Section: Chemical Composition Of Turkey Neck Meatsupporting

confidence: 81%

Section: Chemical Composition Of Turkey Neck Meatsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

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Mass transfer and physicochemical characteristics of turkey neck meat during dry salting

et al. 2020

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“…The results of the NIR spectra of the samples were evaluated with principal component analysis (PCA). With PCA it is possible to break down large amounts of data into a few new variables, which contain the majority of variance of the original data [ 32 ]. It provides information on outliers, specific trends or whether there are groups in the data.…”

Section: Methodsmentioning

confidence: 99%

Determining Sonication Effect on E. coli in Liquid Egg, Egg Yolk and Albumen and Inspecting Structural Property Changes by Near-Infrared Spectra

Nagy

Felföldi²,

Brückner³

et al. 2021

Sensors

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In this study, liquid egg, albumen, and egg yolk were artificially inoculated with E. coli. Ultrasound equipment (20/40 kHz, 180/300 W; 30/45/60 min) with a circulation cooling system was used to lower the colony forming units (CFU) of E. coli samples. Frequency, absorbed power, energy dose, and duration of sonication showed a significant impact on E. coli with 0.5 log CFU/mL in albumen, 0.7 log CFU/mL in yolk and 0.5 log CFU/mL decrease at 40 kHz and 6.9 W absorbed power level. Significant linear correlation (p < 0.001) was observed between the energy dose of sonication and the decrease of E. coli. The results showed that sonication can be a useful tool as a supplementary method to reduce the number of microorganism in egg products. With near-infrared (NIR) spectra analysis we were able to detect the structural changes of the egg samples, due to ultrasonic treatment. Principal component analysis (PCA) showed that sonication can alter C–H, C–N, –OH and N–H bonds in egg. The aquagrams showed that sonication can alter the properties of H2O structure in egg products. The observed data showed that the absorbance of free water (1412 nm), water molecules with one (1440 nm), two (1462 nm), three (1472 nm) and four (1488 nm) hydrogen bonds, water solvation shell (1452 nm) and strongly bonded water (1512 nm) of the egg samples have been changed during ultrasonic treatment.

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Application of colorimetric sensor array coupled with machine‐learning approaches for the discrimination of grains based on freshness

et al. 2023

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BackgroundVolatile organic compounds (VOCs) in grain fluctuate depending on the degree of grain freshness. A new colorimetric sensor array (CSA) was developed as capture probes for the quantification of VOCs in grains in this work, and it was designed to monitor the variation of grain VOCs. CSA spectral data acquisition using visible–near‐infrared spectroscopy and image processing of CSA's image imformation by computer were used comparatively. Then, machine‐learning‐based models – for example, synergistic interval partial least squares, genetic algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and ant colony optimization (ACO) algorithm – were introduced to optimize variables. Moreover, principal component analysis, and linear discriminant analysis (LDA), and K‐nearest neighbors (KNN) were used for the classification. Ultimately, quantitative models for detecting grain freshness are developed using various variable selection strategies.ResultsCompared with the pattern recognition results of image processing, visible–near‐infrared spectroscopy could better separate the grains with different freshness from principal component analysis, and the prediction set of LDA models could correctly identify 100% of rice, 96.88% of paddy, and 97.9% of soybeans. In addition, compared with CARS and ACO, the LDA model and KNN model based on genetic algorithms show the best prediction performance. The prediction set could correctly identify 100% of rice and paddy samples and 95.83% of soybean samples.ConclusionThe method developed could be used for non‐destructive detection of grain freshness. © 2023 Society of Chemical Industry.

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Identification of turkey meat and processed products using near infrared spectroscopy

Cited by 45 publications

References 21 publications

Mass transfer and physicochemical characteristics of turkey neck meat during dry salting

Mass transfer and physicochemical characteristics of turkey neck meat during dry salting

Determining Sonication Effect on E. coli in Liquid Egg, Egg Yolk and Albumen and Inspecting Structural Property Changes by Near-Infrared Spectra

Application of colorimetric sensor array coupled with machine‐learning approaches for the discrimination of grains based on freshness

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