Near-infrared reflectance spectroscopy for the prediction of chemical composition in walnut kernel

In the present work, 116 samples were collected and near-infrared reflectance spectroscopy prediction model for determination of moisture, protein, and fat contents of walnut meal were obtained and evaluated. All the samples were analyzed based on the chemical methods. Meanwhile, they were scanned to obtain their near-infrared reflectance spectrum in the wavelength range of 570–1840 nm. Several preprocess treatments including scattering pretreatments, mathematical pretreatments, and aggression methods were optimized to increase the accuracy of the calibration models according to the coefficient of determination for calibration (R_c²) and the cross-validation (one minus the variance ratio, 1-VR), and the standard error of calibration and cross-validation. The results showed modified partial least square loading was the better aggression method to predict the moisture, proteins, and fats in walnut kernel. The calibration equations obtained indicated that near-infrared reflectance spectroscopy had excellent predictive capacity for the three components with R_c² = 0.965, standard error of calibration = 0.052 for moisture, and R_c² = 0.967, standard error of calibration = 0.191 for proteins, and R_c² = 0.979, standard error of calibration = 0.171 for fats, respectively. The external validation further confirmed the robustness and reliability of the near-infrared reflectance spectroscopy prediction models with the correlation coefficient of actual and predicted values (R²) = 0.952, ratio of performance deviation = 4.14, the standard error of prediction =0.058 for moisture, and R² = 0.977, ratio of performance deviation = 5.55, standard error of prediction = 0.182 for proteins, and R² = 0.990, ratio of performance deviation = 8.64, standard error of prediction = 0.191 for fats, respectively. Near-infrared reflectance spectroscopy is a reliable technology to predict these constituents in walnuts.     

Production tactic and physiochemical properties of low ω6/ω3 ratio structured lipid synthesised from perilla and soybean oil

Structured lipid (SL) was synthesised by enzymatic interesterification of soybean oil and perilla oil. Response surface methodology (RSM) was used to determine the effects of three variables on the lipase‐catalysed interesterification. Based on ridge analysis, combination of reaction time (X₁; 18 h), reaction temperature (X₂; 60 °C), and substrate mole ratio (X₃; 1:1) were optimised for higher incorporation of ω3 (alpha (α) linolenic acid (ALA)) (Y). The predictive model was found to be adequate due to no significant lack of fit and satisfactory level of coefficient of determination (R² = 0.97). Experiments were conducted under optimised conditions which were predicted by the model equations, obtained from RSM yielded SL with linoleic (44.01%) and ALA (35.82%) were detected at sn‐2 position. The effects of antioxidants such as catechin, butylated hydroxytoluene (BHT) BHT and rosemary extracted on the oxidative stability in SL were investigated. Among all antioxidants, the highest stability was obtained from catechin.     

Effects of sample size on neutral detergent fiber digestibility of triticale forages using the Ankom DaisyII Incubator system

Accurate and precise determinations of fiber digestibility are essential for proper diet formulation for dairy cows. Our objectives were 3-fold: (1) regress in vitro neutral detergent fiber digestibility (NDFD) values from 48 triticale forages determined at multiple endpoints ranging from 12 to 240 h with Ankom Daisy^II Incubator system (Ankom Technology Corp., Macedon, NY) methods using 0.25- or 0.50-g sample sizes on concentrations of fiber-related analytes or growth stage; (2) directly compare NDFD values determined with 0.25- or 0.50-g sample sizes by Ankom methods after 12-, 24-, 30-, 48-, 144-, or 240-h incubations; and (3) compare NDFD values determined by Ankom methods after 30 and 48 h of incubation with those determined by traditional sealed-tube procedures obtained from a commercial laboratory. Generally, plant growth stage, which was quantified with a linear model suitable for serving as an independent regression variable, proved to be a better predictor variable for NDFD than neutral detergent fiber or acid detergent lignin. For direct comparisons of 0.25- and 0.50-g sample sizes using Ankom methods, the regression relationship for a 30-h incubation was explained by a linear model [Y = 1.206x – 1.1; coefficient of determination (R²) = 0.933], in which the slope differed from unity, but the intercept did not differ from 0. After a 48-h incubation, a linear model (Y = 1.014x + 7.1; R² = 0.964) indicated that the slope did not differ from unity, but the intercept was >0. A linear regression (Y = 1.040x – 1.8; R² = 0.861) of the 30-h incubation results obtained by Ankom methods using the 0.25-g sample size on those from the commercial laboratory indicated the slope and intercept did not differ from unity or 0, respectively. A similar relationship was obtained from the 48-h incubation (Y = 1.021x – 3.4; R² = 0.866). Relationships were poorer when the 0.50-g sample size was used by Ankom methods, particularly for the 30-h incubation, where the slope (0.824) was less than unity. Generally, NDFD values were greater for the 0.25-g sample size by Ankom methods, especially with 24-, 30-, and 48-h incubation times, and agreement with traditional sealed-tube methods was improved with the smaller sample size. Synchronization of results between Ankom and traditional methods needs to be further verified across a wider range of forages and harvest/preservation methods before definitive recommendations can be made.     

Development of a predictive model describing the growth of Listeria Monocytogenes in Kimbab

This study is to develop mathematical models to predict the growth of Listeria monocytogenes in kimbab as a function of storage temperature. Kimbab which was inoculated with L. monocytogenes were incubated at 4, 10, 15, and 30°C. The primary model showed a good fit (R²=0.9845 to 0.9967) to a Gompertz equation to obtain specific growth rates (SGR) and lag time (LT) at each temperature. The SGR of L. monocytogenes in the kimbab increased and LT decreased by increasing temperature. Secondary polynomial model was developed using PRISM general nonlinear analysis software for SGR and LT. The secondary models were 0.1479−(0.02457×Temp)+(0.001296 ×Temp²) for SGR and 312.8−(30.21×Temp)+(0.6654 ×Temp²) for LT. This secondary polynomial model was judged as appropriate based on the coefficient of determination (R² of the SGR and LT model=0.9995, 0.9556), the bias factor (B _f of the SGR and LT model=0.97, 0.94), and the accuracy factor (A _f of the SGR and LT model=1.10, 1.68). Reliable predictions of L. monocytogenes SGR and LT in kimbab were based on temperature.     

Fish protein hydrolysates from gold carp (Carassius auratus): I. A study of hydrolysis parameters using response surface methodology

The combined effects of pH, temperature (T), substrate/buffer ratio (S:B) and enzyme concentration (E) on protein recovery from gold carp (Carassius auratus) processing waste with Flavourzyme^® were characterised. The effect of hydrolysis parameters on the degree of hydrolysis (DH) was described through response surface analysis (RSA) and the model obtained was defined as follows: DH = ?316.30 + 7.1T + 44.5pH + 0.292S:B + 0.27E ? 0.048T² ? 2.407pH² ? 0.002S:B² ? 0.307TxpH ? 0.002S:BxE. All regression coefficients were significant (α = 0.05). The model showed a good fit with the experimental data, since the R² of 0.923 indicated that 92.3% of the variability within the range of values studied could be explained by the model. The mathematical model presented a plateau region with a maximum DH (>26.6%) at the following critical values: pH = 5.9, T = 53 °C, S:B = 14.7% and E = 80 LAPU (leucine aminopeptidase units) g^?1. Electrophoretic (SDS‐PAGE) patterns showed a progressive reduction in molecular weight of the peptidic fractions from 10 min of hydrolysis onwards. By controlling the enzymatic process, it was possible to predict the molecular weight of the peptides obtained, in turn affecting the functional and nutritional properties of the peptidic fractions. Copyright © 2004 Society of Chemical Industry     

Sarcoplasmic Protein Profile from Drip Loss in Relation to Pork Quality

The aim of this research was to correlate the sarcoplasmic protein profile of the natural drip loss of muscle with the technological and sensory quality of pork meat. Material for analysis was taken from 14 PenArLan pigs. Simple analysis of relationships between the test protein and meat quality traits showed that they adopt a range of from –0.58 to 0.72. It has been shown that the significance of glycolysis expressed as level of glycogen and lactic acid may be 86% (coefficient of correlations, C_R = 0.93), explained by the amount of certain enzymes. The higher relation has been obtained between studied enzymes and value of pH₂₄, L^* and b^* parameters of raw meat (98%; C_R = 0.99). By contrast, L^* and a^* parameters, sour flavor, and tenderness of grilled meat in 79% can be explained based on the content of the enolase, glyceraldehyde‐3‐phosphate dehydrogenase, and phosphoglycerate mutase (C_R = 0.89). The results of the multiple regression showed that amount of lactic acid was related to the quantity of the 3 enzymes: phosphofructokinase, AMP deaminase, phosphorylase b/phosphorylase b kinase (r = 0.88).     

Mathematical models of pretreatment processes to utilize purple-fleshed potato (<Emphasis Type="Italic">Solanum tuberosum</Emphasis> L.) peels for anthocyanin extraction

The effect of particle size on the extraction rate of anthocyanin from Solanum tuberosum L. (purple-fleshed potato) peels (PFPPs) using the stagnant single-stage batch extraction system was investigated. Core pretreatment processes such as drying and grinding were quantitatively evaluated using mathematical models. The drying behavior of PFPP was successfully described using thin layer models (Page model and Midilli-Kucuk model). The effective diffusion coefficient of drying temperature at 40°C was determined to be 1.67x10^?12 m²/s. The grinding time to obtain particles of a specific size was accurately estimated using the grinding kinetic model (R ²=0.97). The extraction rate of anthocyanin increased as the particle size decreased; however, when the particle size was 0.15mm, the anthocyanin content decreased. Our study demonstrated that the grinding kinetic model is useful to estimate the grinding time to produce an optimum particle size for anthocyanin extraction from PFPP.     

Batch kinetics and modelling of ethanolic fermentation of whey

The fermentation of whey by Kluyveromyces marxianus strain MTCC 1288 was studied using varying lactose concentrations at constant temperature and pH. The increase in substrate concentration up to a certain limit was accompanied by an increase in ethanol formation, for example, at a substrate concentration of 10 g L^?1, the production of ethanol was 0.618 g L^?1 whereas at 50 g L^?1 it was 3.98 g L^?1. However, an increase in lactose concentration to 100 g L^?1 led to a drastic decrease in product formation and substrate utilization. The maximum ethanol yield was obtained with an initial lactose concentration of 50 g L^?1. A method of batch kinetics was utilized to formulate a mathematical model using substrate and product inhibition constants. The model successfully simulated the batch kinetics observed at S₀ = 10 and 50 g L^?1 but failed in case of S₀ = 100 g L^?1 because of strong substrate inhibition.     

Utilisation of front face fluorescence spectroscopy as a tool for the prediction of some chemical parameters and the melting point of semi-hard and hard cheeses: a preliminary study

The objective of this preliminary study was to evaluate the usefulness of front face fluorescence spectroscopy to predict some chemical parameters [pH, fat, dry matter (DM), fat in DM, total nitrogen (TN) and water soluble nitrogen (WSN)] and cheese meltability of semi-hard and hard cheeses. Dynamic testing rheology was used to determine the melting point of cheeses corresponding to the temperature at which loss tangent (tan δ) = 1. Tryptophan and vitamin A fluorescence spectra were, also, recorded on cheese samples at 20 °C. The partial least squares (PLS) regression with the leave one-out cross-validation technique was used to build up calibration models. Excellent predictions were obtained from the tryptophan and vitamin A models for fat (R ² = 0.99 and 0.97, respectively), DM (R ² = 0.94 and 0.96, respectively), fat in DM (R ² = 0.92 and 0.99, respectively), TN (R ² = 0.91 and 0.91, respectively). Excellent predictions were also obtained for WSN (R ² = 0.96) and melting point (R ² = 0.97) from vitamin A spectra, while only good predictions for these two parameters (R ² = 0.90 and R ² = 0.87, respectively) were obtained from tryptophan spectra. The results for pH were good (R ² = 0.82) and approximate (R ² = 0.76) with tryptophan and vitamin A, respectively.     

Thermal inactivation of polyphenoloxidase and peroxidase in green coconut (Cocos nucifera) water

Coconut water is an isotonic beverage naturally obtained from the green coconut. After extracted and exposed to air, it is rapidly degraded by enzymes peroxidase (POD) and polyphenoloxidase (PPO). To study the effect of thermal processing on coconut water enzymatic activity, batch process was conducted at three different temperatures, and at eight holding times. The residual activity values suggest the presence of two isoenzymes with different thermal resistances, at least, and a two‐component first‐order model was considered to model the enzymatic inactivation parameters. The decimal reduction time at 86.9 ^°C (D_{86.9 °C}) determined were 6.0 s and 11.3 min for PPO heat labile and heat resistant fractions, respectively, with average z‐value = 5.6 °C (temperature difference required for tenfold change in D). For POD, D_{86.9 °C} = 8.6 s (z = 3.4 °C) for the heat labile fraction was obtained and D_{86.9 °C} = 26.3 min (z = 6.7 °C) for the heat resistant one.     

An Improvement of the Cell Diffusion Method for the Rapid Determination of Diffusion Constants in Gels or Foods

A diffusion cell was designed to study the apparent diffusion coefficient of solutes through foodstuffs. The mathematical model corresponding to the experimental setup did not assume quasi-steady state diffusion within the slice of food. It was used to obtain the effective diffusion coefficient of NaCl through 3% Agar gels. The results were compared to those obtained by using two agar gel cylinders at different concentrations. The diffusion coefficient of Cl^? in gel at 25°C was obtained with at least the same precision (D = 1.30 × 10^?9 m²/sec, S_D= 0.03 × 10^?9 m²/sec) within less than 110 min compared to a few hours with the two cylinder technique. With the mathematical model used to determine diffusion coefficients, the equilibrium ratio between solute concentration in solution and at the interface of foodstuff can be determined.     

Predicting the colour properties of viscose knitted fabrics using soft computing approaches

The aim of this paper was to predict the colour strength of viscose knitted fabrics by using fuzzy logic (FL) model based on dye concentration, salt concentration and alkali concentration as input variables. Moreover, the performance of fuzzy logic (FL) model is compared with that of artificial neural network (ANN) model. In addition, same parameters and data have been used in ANN model. From the experimental study, it was found that dye concentration has the main and greatest effects on the colour strength of viscose knitted fabrics. The coefficient of determination (R²), root mean square (RMS) and mean absolute errors (MAE) between the experimental colour strength and that predicted by FL model are found to be 0.977, 1.025 and 4.61%, respectively. Further, the coefficient of determination (R²), root mean square (RMS) and mean absolute errors (MAE) between the experimental colour strength and that predicted by ANN model are found to be 0.992, 0.726 and 3.28%, respectively. It was found that both ANN and FL models have ability and accuracy to predict the fabric colour strength effectively in non-linear domain. However, ANN prediction model shows higher prediction accuracy than that of Fuzzy model.     

Combination of emerging technologies for the extraction of bioactive compounds

Abstract

With the growing consumer demands for greener alternatives that do not involve toxic chemicals as well as the industry concerns of sustainable, nontoxic routes of extraction, the applications of novel extraction technologies in the food industry have been widely studied. This review discussed the novel extraction technologies including their mechanisms, protocols, influencing factors, advantages and drawbacks, as well as a comprehensive summary of the combination of the novel extraction technologies for phyto-bioactive compounds. Novel extraction methods, including ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE) and enzyme-assisted extraction (EAE), are considered as clean, green and efficient alternative to conventional extraction technologies. Their combinations, ultrasound-assisted enzymatic extraction (UAEE), microwave-assisted enzymatic extraction (MAEE) and ultrasonic microwave-assisted extraction (UMAE), can exhibit higher potential extraction ability. However, some of them need specific equipment. The food industry in the extraction sector should choose a proper extraction method which has a balance between product quality, process efficiency, production costs and environmentally friendly processes. The current review presented comprehensive references for future research on the novel extraction of phyto-bioactive compounds extraction.

1. Highlights

2. Novel clean, green and efficient alternative to conventional extraction technologies are discussed.

3. Combination of the novel extraction technologies for synergistic effects.

4. Minimal degradation and enhanced extraction yields.

5. Extraction mechanisms, advantages and drawbacks associated with novel extraction technologies.

     

Non-Destructive Estimation of Mechanical and Chemical Properties of Persimmons by Ultrasonic Spectroscopy

Fruit properties, including ripeness, are still being measured destructively in most countries. Recently, an increased attention has been given to non-destructive methods, such as ultrasonic techniques, for quality assessment of food commodities. The aim of this study was to evaluate the feasibility of ultrasonic spectroscopy to monitor the changes of mechanical properties (rupture force and modulus of elasticity) and chemical properties (soluble solids content) in two cultivars of persimmons (Karaj and Shomali) during 3 weeks of storage. Samples’ ultrasonic properties such as ultrasonic velocity and attenuation coefficient were analyzed with transducers, and their textural properties were obtained using destructive compression test and their refractometric properties were measured from its fresh-squeezed juice in laboratory. Mechanical and chemical properties were statistically modeled from ultrasonic parameters. The results showed that our proposed model could predict persimmons’ mechanical and chemical properties well, i.e., rupture force (R² = 0.893/0.837; RMSE = 3.683/3.849 for Shomali and Karaj cultivars, respectively), modulus of elasticity (R² = 0.826/0.861; RMSE = 0.231/0.217 for Shomali and Karaj cultivars, respectively) and soluble solids content (R² = 0.931/0.987; RMSE = 0.519/0.851 for Shomali and Karaj cultivars, respectively).     

Production of lipase‐catalyzed solid fat from mustard oil and palm stearin with linoleic acid by response surface methodology

BACKGROUND: Solid fat was produced from mustard oil and palm stearin through lipase‐catalyzed reaction, in which linoleic acid was intentionally incorporated. For optimizing the reaction condition of melting point and ω6/ω3 fatty acids, response surface methodology (RSM) was employed with three reaction variables such as substrate mole ratio of mustard oil (MO) to palm stearin (PS) (X₁), reaction temperature (X₂) and reaction time (X₃). RESULTS: The predictive model for melting point of solid fat was adequate and reproducible due to no significant lack of fit (P = 0.0764), P‐value (0.0037) of the model, and satisfactory level of coefficient of determination (R² = 0.92). For the ω6/ω3 ratio model, R² and P‐value were 0.89 and 0.0132, respectively, but lack of fit was significant (P = 0.0389). The melting point of the produced solid fat was affected by substrate mole ratio, whereas reaction temperature and time had no significant effect. The ω6/ω3 ratio of solid fat was influenced by substrate mole ratio and reaction temperature but not by reaction time. Based on ridge analysis, lower ω6/ω3 ratio was predicted by decreasing substrate mole ratio and reaction time, and by increasing reaction temperature. CONCLUSIONS: For producing solid fat with a specific melting point of 34.57 °C, a combination of 1:2 (X₁), 65.17 °C (X₂) and 21.46 h (X₃) was optimized, and the optimization was confirmed under the same reaction conditions. The solid fat contained palmitic (37.8%), linoleic (24.8%), oleic (21.3%), and erucic acid (9.7%), and its solid fat content was 30.3% and 10.3% at 20 and 30 °C, respectively. Copyright © 2009 Society of Chemical Industry     

Feasibility of isolation and identification of aroma organisms from home-made fermented milk

The purpose of the experiment was to attempt the isolation and identification of lactic acid bacteria content of locally fermented milk, and the feasibility of such organisms to produce diacetyl plus acetoin by single, double, and multiple combinations of identified bacteria. The following results were obtained.

• 1 An addition of 0.3 % yeast extract to broth media and to skimmed milk was useful and satisfactory.
• 2 Seven lactic acid bacteria were isolated and identified.
• 3 The activities of identified organisms were as follows: The viable cell counts ranged from 6.5 · 10⁸ bis 10.5 · 10⁸ cells/ml, culture potential acidities were 0.15-2.52%, and the time of coagulation and reduction was 16-60 h, except Leu. citrovorum which showed negative reaction.
• 4 It was advisable to use Na-citrate with skimmed milk to confirm the positive results prior to the main experiments.
• 5 Single bacterium culture produced diacetyl plus acetoin as arange 0.79-3.50 mg/100g culture, except L. bulgaricus and L. casei.
• 6 Cultures of double bacteria combinations showed satisfactory productions of aroma materials. The range was 2.82-3.60 mg/100 g culture. Synergistic effect was notable between almost all combinations of organisms.
• 7 Multiple combination cultures showed in general abiodeteriorative effect among individual organisms of the same goup, however, some groups which were active yielded 3.6-8.36 mg/100 g aroma materials.

     

Determination of dry matter and crude protein contents of undried forages by near‐infrared reflectance spectroscopy

Near‐infrared reflectance spectroscopy (NIRS) was used to predict the dry matter (DM) and crude protein (CP) contents of untreated forage samples. Four hundred forage samples were analysed in reflectance mode. Two mathematical treatments based on the order of derivative of log(1/R), the gap in data points and the numbers of data points used in the first and second smoothings were applied. Predictive equations were developed using modified partial least squares (MPLS) with internal cross‐validation. The coefficient of determination of calibration and the standard error of cross‐validation (SECV, in parentheses) for DM were 0.92 (12.4), 0.92 (12.6) and 0.93 (11.7) for the two treatments and log(1/R) respectively on a g kg^?1 fresh weight basis. For CP the NIRS calibration statistics yielded and SECV (in parentheses) values of 0.85 (19.8), 0.85 (19.5) and 0.87 (18.1) for the two treatments and log(1/R) respectively on a g kg^?1 fresh weight basis. It was concluded that NIRS is a suitable method to predict the dry matter and crude protein contents of fresh forage without sample preparation. © 2002 Society of Chemical Industry     

Qualitative and Quantitative Prediction of Volatile Compounds from Initial Amino Acid Profiles in Korean Rice Wine (makgeolli) Model

In Korean rice wine (makgeolli) model, we tried to develop a prediction model capable of eliciting a quantitative relationship between initial amino acids in makgeolli mash and major aromatic compounds, such as fusel alcohols, their acetate esters, and ethyl esters of fatty acids, in makgeolli brewed. Mass‐spectrometry‐based electronic nose (MS‐EN) was used to qualitatively discriminate between makgeollis made from makgeolli mashes with different amino acid compositions. Following this measurement, headspace solid‐phase microextraction coupled to gas chromatography‐mass spectrometry (GC‐MS) combined with partial least‐squares regression (PLSR) method was employed to quantitatively correlate amino acid composition of makgeolli mash with major aromatic compounds evolved during makgeolli fermentation. In qualitative prediction with MS‐EN analysis, the makgeollis were well discriminated according to the volatile compounds derived from amino acids of makgeolli mash. Twenty‐seven ion fragments with mass‐to‐charge ratio (m/z) of 55 to 98 amu were responsible for the discrimination. In GC‐MS combined with PLSR method, a quantitative approach between the initial amino acids of makgeolli mash and the fusel compounds of makgeolli demonstrated that coefficient of determination (R²) of most of the fusel compounds ranged from 0.77 to 0.94 in good correlation, except for 2‐phenylethanol (R² = 0.21), whereas R² for ethyl esters of MCFAs including ethyl caproate, ethyl caprylate, and ethyl caprate was 0.17 to 0.40 in poor correlation.