Determination of Total Sugar Content in Soy-Based Drinks Using Infrared Spectroscopy and Chemometrics

Partial least square (PLS) regression models were developed and compared in order to determine the total sugar content in soy-based drinks using an infrared spectroscopy technique known as attenuated total reflectance Fourier transform infrared (ATR-FTIR). On a spectrophotometer set for analyzing on the middle infrared region, spectral band of 1900 to 900 cm^?1, commercial samples of soy beverage were analyzed, as well as samples with crescent water additions of 5, 10, and 20% v/v. Reference data for total sugars were obtained using the Lane-Eynon method. To construct regression models, algorithms of interval partial least square (iPLS) and synergy of interval partial least square (siPLS) were applied using iToolbox package on Matlab 8.1 environment. Kennard-Stone algorithm was used to the selection of calibration and prediction sets. Two models have been the best obtained: the first was an iPLS with seven latent variables, which selected the spectral band of 1399–900 cm^?1 and presented root mean square error of cross-validation (RMSECV)?=?0.1678% (w/w). The second best model was siPLS with six latent variables, which selected spectral bands of 1025–1150 and 1151–1476 cm^?1 and presented RMSECV?=?0.1963% (w/w). The proposed method presents advantages such as a small-required amount of sample for spectrum achievement, no sample destruction, and a high analytical frequency.     

Simultaneous analysis of main catechins contents in green tea (Camellia sinensis (L.)) by Fourier transform near infrared reflectance (FT-NIR) spectroscopy

This paper reported the results of simultaneous analysis of main catechins (i.e., EGC, EC, EGCG and ECG) contents in green tea by the Fourier transform near infrared reflectance (FT-NIR) spectroscopy and the multivariate calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The number of PLS factors and the spectral preprocessing methods were optimised simultaneously by cross-validation in the model calibration. The performance of the final model was evaluated according to root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (R). The correlations coefficients (R) in the prediction set were achieved as follows: R = 0.9852 for EGC model, R = 0.9596 for EC model, R = 0.9760 for EGCG model and R = 0.9763 for ECG model. This work demonstrated that NIR spectroscopy with PLS algorithm could be used to analyse main catechins contents in green tea.     

Novel Prediction of Total Fat Content in Cocoa Beans by FT-NIR Spectroscopy Based on Effective Spectral Selection Multivariate Regression

Total fat content is a major quality parameter that chocolate manufactures consider when selecting cocoa beans. This paper attempted the feasibility of measuring total fat content in cocoa beans by using Fourier transform near-infrared (FT-NIR) spectroscopy based on a novel systematic study on efficient spectral variables selection multivariate regression. After the efficient spectra interval selection by synergy interval partial least squares (Si-PLS), the data were treated with support vector machine regression (SVMR) leading to synergy interval support vector machine regression (Si-SVMR). Experimental results showed that the model based on the novel Si-SVMR algorithm was superior to the others. The optimum results were assessed by root-mean-square error of prediction (RMSEP) and correlation coefficient (R _pre) in the prediction set. The performance of Si-SVMR model was RMSEP?=?0.015 and R _pre?=?0.9708. This study has demonstrated that the total fat content in cocoa beans could rapidly be predicted by FT-NIR spectroscopy and Si-SVMR technique. The novel strength and accuracy of Si-SVMR in contrast to other multivariate algorithms has been derived.     

Effects of gardenia seed,green tea,and cactus pear in rice batter on the chemical quality of lotus root <Emphasis Type="Italic">bugak</Emphasis> and frying oil

This study investigated the effects of the addition of gardenia seed, green tea, or cactus pear (Opuntia ficus-indica) to rice batter at 10% on the lipid oxidation, pigments, antioxidants, and antioxidant activity of lotus root bugak and frying oil. Lipid oxidation was evaluated based on the conjugated dienoic acid and p-anisidine values. Lipid oxidation and tocopherol degradation were significantly reduced in the gardenia seed-added bugak and frying oil, whereas the cactus pear-added bugak and frying oil showed an increase. The addition of green tea had no significant effects on the lipid oxidation of bugak and frying oil. The in vitro antioxidant activity of lotus root bugak significantly increased with the addition of gardenia seed, green tea, or cactus pear. The results suggested that green tea and gardenia seed could improve the health and food functionality of antioxidation for lotus root bugak, respectively.     

Mapping the Pungency of Green Pepper Using Hyperspectral Imaging

The pungency level of green peppers is dependent on the amounts of capsaicin and dihydrocapsaicin they contain. This study was conducted to develop a non-destructive method for the prediction and mapping of the capsaicin and dihydrocapsaicin contents in green pepper. Hyperspectral images of 200 total green peppers of three varieties were acquired in the wavelength range of 1000–1600 nm, from which the mean spectra of each pepper variety were extracted. The reference capsaicin and dihydrocapsaicin contents of the samples were measured by high-performance liquid chromatography. Quantitative calibration models were built using partial least squares (PLS) regression with different spectral preprocessing techniques; the best performance was found by normalizing the preprocessed spectra with correlation coefficients (r_pred) of 0.86 and 0.59, which showed the standard errors of prediction (SEPs) of 0.09 and 0.03 mg/g for capsaicin and dihydrocapsaicin, respectively. Seventeen and 16 optimal wavebands were selected using the successive projections algorithm; r_pred of 0.88 and 0.68 and SEPs of 0.084 and 0.027 mg/g were obtained for capsaicin and dihydrocapsaicin, respectively, from the newly developed PLS calibration models using these optimal wavebands. The successive projections algorithm (SPA)-PLS model was used to map the capsaicin and dihydrocapsaicin contents of the green peppers. These maps provided detailed information on the pungency levels of the tested green peppers. The results of this study indicated that hyperspectral imaging is useful for the rapid and non-destructive evaluation of the pungency of green peppers.     

Determination of Three Alcohols in Chinese Dukang Base Liquor by FT-NIR Spectroscopy

Alcohols are important aroma compounds in Chinese liquors. In this work, 3-methyl-1-butanol, 1-butanol, and 1-propanol in Dukang base liquor were simultaneously analyzed by gas chromatography (GC) and fourier-transform near-infrared (FT-NIR) spectroscopy. The optimal combinations of spectral intervals for three alcohols were selected for modeling. The calibration models, which are based on FT-NIR spectral variables and the chemical values, were established with partial least square (PLS) and validated using internal cross validation. In calibration set, the coefficients of determination (R ²) for 1-propanol, 1-butanol, and 3-methyl-1-butanol were 95.21, 98.05, and 98.05, respectively; corresponding root mean square errors of calibration (RMSEC) were 0.27, 0.49, and 0.67 mg per 100 mL. In validation set, the R ² were 94.72, 97.96, and 95.22; the root mean square errors of prediction (RMSEP) were 0.40, 0.81, and 1.35 mg per 100 mL. The results indicated that the correlation between the values determined by GC and the values estimated by the calibration for the three alcohols was excellent. The FT-NIR spectroscopy calibration models, which with good prediction performance and high precision, could be used as a rapid methods for determination of alcohols in Chinese liquor.     

Determination of Pear Internal Quality Attributes by Fourier Transform Near Infrared (FT-NIR) Spectroscopy and Multivariate Analysis

This paper attempted the feasibility to determine firmness and soluble solid content (SSC) in intact pears using Fourier transform near infrared (FT-NIR) spectroscopy coupled with multivariate analysis. Principal component analysis and independent component analysis were employed comparatively to extract latent vectors from the original spectra data. Extreme learning machine (ELM) was performed to calibrate regression model. Some parameters of ELM model were optimized according to the lowest root mean square error of cross-validation in the calibration set. Moreover, the root mean square error of prediction of the calibration model was finally corrected for making it more closed to the true prediction error due to the effect of reference measurement error existing in the pear sample attribute value on the prediction error of the model. Experimental results showed that the $ R_p^2 $ and ratio performance deviation (RPD) in the prediction set were achieved as follows: $ R_p^2 $ ?=?0.81 and RPD?=?2.28 for the firmness model when ICs?=?6 and $ R_p^2 $ ?=?0.91 and RPD?=?3.43 for the SSC model when ICs?=?5. This study demonstrates that the predictive precision of the calibration model can be effectively enhanced in measurement of firmness and SSC in intact pears by use of FT-NIR spectroscopy combined with appropriate chemometrics methods.     

A mathematical model for moisture movement during continous and intermittent drying of <Emphasis Type="Italic">Eucalyptus saligna</Emphasis>

A modified diffusion-based mathematical model is proposed to describe the moisture movement during continuous and intermittent drying of Eucalyptus saligna. This model includes the temperature change, the surface drying coefficient (β _n ) and 2 diffusion coefficients [from green to FSP (D _f ) and from FSP to dry condition (D _o )] as important parameters. The final model expression obtained was M?=?exp (??25 β _n ² D _t /l²) with the β _n used was 1.5807 kg m^?2 s^?1, the D _f was 2.26?×?10^?11 m² s^?1, and the D _o was 5.85?×?10^?12 m² s^?1. The range of temperature change between heating and non-heating phases in the intermittent drying regimes was from 24.9 to 31.8 °C. The R² values obtained when the model was fitted into the drying data of different intermittent regimes ranged from 71.5 to 85.9%. The R² value was 87.4% when the model was fitted into continuous trial data. The high values of R² indicate that the model can be used to understand the moisture reduction both in intermittent and continuous regimes.     

Quantifying Total Viable Count in Pork Meat Using Combined Hyperspectral Imaging and Artificial Olfaction Techniques

Total viable count (TVC) of bacteria is one of the most important indexes in evaluation of quality and safety of meat. This study attempts to quantify the TVC content in pork by combining two nondestructive sensing tools of hyperspectral imaging (HSI) and artificial olfaction system based on the colorimetric sensor array. First, data were acquired using HSI system and colorimetric sensors array, respectively. Then, the individual characteristic variables were extracted from each sensor. Next, principal component analysis (PCA) was used to achieve data fusion based on these characteristic variables from two different sensor data for further multivariate analysis. In developing the models, linear (PLS and stepwise MLR) and nonlinear (BPANN and SVMR) pattern recognition methods were comparatively employed, and they were optimized by cross-validation. Compared with other models, the SVMR model achieved the best result, and the optimum results were achieved with the root mean square error of prediction (RMSEP)?=?2.9913 and the determination coefficient (R _p )?=?0.9055 in the prediction set. The overall results showed that it has the potential in nondestructive detection of TVC content in pork meat by integrating two nondestructive sensing tools of HSI and colorimetric sensors with SVMR pattern recognition tool.     

Prediction of Cadmium content in brown rice using near‐infrared spectroscopy and regression modelling techniques

The feasibility of prediction of cadmium (Cd) content in brown rice was investigated by near‐infrared spectroscopy (NIRS) and chemometrics techniques. Spectral pretreatment methods were discussed in detail. Synergy interval partial least squares (siPLS) algorithm was used to select the efficient combinations of spectral subintervals and wavenumbers during constructing the quantitative calibration model. The performance of the final model was evaluated by the use of root mean square error of cross‐validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficients for calibration set and prediction set (R_c and R_p), respectively. The results showed that the optimum siPLS model was achieved when two spectral subinterval and fifty‐two variables were selected. The predicted result of the best model obtained was as follows: RMSECV = 0.232, R_c = 0.930, RMSEP = 0.250 and R_p = 0.915. Compared with PLS and interval PLS models, siPLS model was slightly better than those methods. These results indicate that it is feasible to predict and screen Cd content in brown rice using NIRS.     

Application of Near-Infrared Hyperspectral Imaging with Variable Selection Methods to Determine and Visualize Caffeine Content of Coffee Beans

Hyperspectral imaging covering the spectral range of 874–1734 nm was used to determine caffeine content of coffee beans. Spectral data of 958.24–1628.89 nm were extracted and preprocessed. Partial least squares regression (PLSR) model on the preprocessed full spectra obtained good performance with coefficient of determination of prediction (R ² _p ) of 0.843 and root mean square error of prediction (RMSEP) of 131.904 μg/g. In addition, 10 variable selection methods were applied to select the best optimal wavelengths. The PLSR models on the different optimal wavelengths obtained satisfactory results. The PLSR model on the wavelengths selected by random frog (RF) performed the best, with R ² _p of 0.878 and RMSEP of 116.327 μg/g. The RF wavelength selection combined with the PLSR model also achieved satisfactory visualization of caffeine content between different coffee beans. The overall results indicated that optimal wavelength selection was an efficient method for spectral data preprocessing, and hyperspectral imaging was illustrated as a potential technique for real-time online determination for caffeine content of coffee beans.     

Ridgetail White Prawn (<Emphasis Type="Italic">Exopalaemon carinicauda</Emphasis>) K Value Predicting Method by Using Electronic Nose Combined with Non-linear Data Analysis Model

In this paper, ridgetail white prawn (Exopalaemon carinicauda) K value predicting model by electronic nose (EN) was studied. Human sensory evaluation (HSE), weight loss, color, total viable counts (TVC), GC-MS, and K value were examined to provide quality references for EN detection. EN responses to prawns were recorded and processed by principal component analysis (PCA) and stochastic resonance (SR). Results indicated that prawn K value rapidly increased due to microbiology propagation. The volatile gases emitted by prawns increased with the increase of storage time based on GC-MS results. PCA method could not discriminate the prawns in different qualities, and SR signal-to-noise ratio (SNR) maximum (SNR_max) values successfully discriminated all samples. K value predicting model was developed by linear fitting regression between K values and SNR_maxvalues (R²?=?0.97). The proposed method will promote the applications of EN in aquatic product quality rapid determination.     

Non-destructive Assessment of Guava (<Emphasis Type="Italic">Psidium guajava</Emphasis> L.) Maturity and Firmness Based on Mechanical Vibration Response

Storage potential and eating quality of guava (Psidium guajava L.) fruit depend on its maturity. Segregation of guava according to maturity and firmness measured using non-destructive technologies would help the industry to designate ripe fruit to immediate market and less ripe fruit for distant market (e.g., exportation). This research was conducted to evaluate the potential of experimental resonant frequency (f _e) and elasticity index (EI) to estimate fruit firmness, which has been reported to be inversely correlated to its maturity. A maturity index (I _m) was calculated as the ratio of total soluble solids/titratable acidity (TSS/TA). It was proved that TSS, TA, and I _m were significantly correlated (P?<?0.05) to skin firmness (F _s), flesh firmness (F _f), stiffness (S), and analytical resonant frequency (ω _n ), being S the attribute best fitted to I _m (R ²?=?0.77). Since it was observed that f _e and EI were sensitive to changes in fruit firmness, both of them were explored as alternatives to predict F _s, F _f, S, and ω _n of guava fruit. In some cases, EI improved the models to predict guava firmness traits (e.g., F _s vs f _e had a coefficient of determination of R ²?=?0.58, whereas for F _s vs EI, it was R ²?=?0.62). The best model occurred when plotting ω _n vs f _e (R ²?=?0.86), followed by S vs EI (R ²?=?0.84), making these promising features for the development of a new practical application using frequency response measurement as a non-destructive method to assess guava maturity.     

FTNIR-A Robust Diagnostic Tool for the Rapid Detection of <Emphasis Type="Italic">Rhyzopertha dominica</Emphasis> and <Emphasis Type="Italic">Sitophilus oryzae</Emphasis> Infestation and Quality Changes in Stored Rice Grains

Fourier transform infrared spectroscopy (FTNIR) is an excellent mode for evaluation of grain-quality attributes. It enables the nonperturbative molecular information to be diagnosed and allows the explication of images of grains by the passage of the spectral data through an array of computational algorithms. The images are contrived from fingerprint spectra so the conception is that the reflection can conceal the status of the analyzed sample. Rhyzopertha dominica F.- and Sitophilus oryzae-infested and fresh rice grains analyzed with FTNIR within a range of 12,000–4000 cm^?1 were proffered to mathematical processing. Partial least squares regression (PLSR) was used for the estimation of physicochemical parameters of rice grains. Outstanding predictive results were acquired denoting that infested rice grains could be convincingly quantified. The coefficient of correlation, root mean square error of validation, and cross validation for the FTNIR model developed to quantify the quality attribute changes with infestation in rice grains were in the range of 99.85–99.01% (R²), 0.2–1.14% (RMSEE), and 0.3–1.25% (RMSECV). Excellent prediction results of various physico-chemical attributes were obtained for rice grains indicating the fresh and infested samples can be uniquely identified. Also, a paired t test was performed to compare the analytical methods with FTNIR-developed method; no significant difference was found (t_cal 0.025?<?t_cri 2.12; α?=?0.05, RPD >?6) between the two methods. Thus, the results further confirmed the developed FTNIR system to be inherently rapid, clean, and capable of preventing hazardous chemicals which originates from traditional analytical processes and has the potential for monitoring and sorting of rice grains.     

Effects of two enzyme extracts of <Emphasis Type="Italic">Aspergillus niger</Emphasis> on green tea aromas

Green tea was investigated in terms of its aroma changes induced by two enzyme extracts of Aspergillus niger, i.e., crude enzyme extracted from fermentation using tea stalk medium (CETSM) and crude enzyme yielded in potato dextrose medium. The result showed that the former had significant effects on sensory indexes and volatile constituents, with significant increases in toasty and mushroom notes, while the latter had little influence on the aforementioned indexes. In addition, the volatile constituents were significantly affected; in particular, the contents of cis-3-hexenol, 1-octen-3-ol, eucalyptol, hexanol, and benzaldehyde increased. Furthermore, gas chromatography–olfactometry (GC–O) analysis showed that an increase in 1-octen-3-ol strengthened the mushroom note. These results indicate that CETSM contains some novel enzymes that can modify the aroma profile of green tea. This study also provides valuable information and suggestions to use fermented enzymes to modify food aromas.     

Feasibility study on the use of Fourier transform near-infrared spectroscopy together with chemometrics to discriminate and quantify adulteration in cocoa beans

Fourier transform near-infrared (FT-NIR) spectroscopy combined with Support Vector Machine (SVM) and synergy interval partial least square (Si-PLS) was attempted in this study for cocoa bean authentication. SVM was used to develop an identification model to discriminate between fermented cocoa beans (FC), unfermented cocoa beans (UFC) and adulterated cocoa bean (5–40 wt/wt.% content of UFC). Si-PLS model was used to quantify the addition of UFC in FC. SVM model accurately discriminated the cocoa bean samples used. After cross-validation, the optimal identification rate was 100% in both the training set and prediction set at three principal components. For quantitative analysis, Si-PLS model was evaluated according to root mean square error of prediction (RMSEP) and coefficient of correlation in prediction (R_pred). The results revealed that Si-PLS model in this work was promising. The optimal performance of Si-PLS model showed an excellent predictive potential, RMSEP = 1.68 and R_pred = 0.98 in the prediction set. The overall results indicated that FT-NIR spectroscopy together with an appropriate multivariate algorithm could be employed for rapid identification of fermented and unfermented cocoa beans as well as the quantification of UFC down to 5% in FC for quality control management.     

Changes in major polyphenolic compounds of tea (<Emphasis Type="Italic">Camellia sinensis</Emphasis>) leaves during the production of black tea

The objective of this study was to quantify the polyphenolic compounds present in tea samples during black tea processing, and to determine the correlation between the contents of individual catechins and theaflavins. Nine monomeric and four dimeric compounds were identified and quantified by HPLC. During black tea processing, the catechins content decreased, whereas the gallic acid content increased. The decrease in the catechins-in particular, the cis-catechins-was due to the formation of dimeric theaflavins. In the present study, we found a significant negative correlation between the changes in the catechins and theaflavins contents during black tea processing. Theaflavin-3-gallate showed the strongest correlations with the cis-catechins ((?)-epigallocatechin: r=0.713; (?)-epicatechin: r=0.755; (?)-epigallocatechin gallate: r=0.681; and (?)-epicatechin gallate: r=0.771).     

Use of Protein Hydrolysate from Yellow Stripe Trevally (<Emphasis Type="Italic">Selaroides leptolepis</Emphasis>) as Microbial Media

The objective of this study was to investigate the potential use of protein hydrolysate from yellow stripe trevally as a nitrogen source for the growth of different microorganisms. Protein hydrolysates from yellow stripe trevally with different degrees of hydrolysis (5, 15 and 25%) prepared using Alcalase (HA) or Flavourzyme (HF) were determined in comparison with commercial Bacto Peptone. For bacteria, Staphylococcus aureus and Escherichia coli, HF with 25% DH (HF₂₅) yielded the highest cell density and specific growth rate (μ _max) and the lowest generation time (t _d) (p?Saccharomyces cerevisiae and Candida albicans, Bacto Peptone yielded the higher growth rate than did HA and HF (p?μ _max and t _d were observed for fungus, Aspergillus oryzae (p?>?0.05). The pH of culture broth containing HF₂₅ decreased markedly during the first 8 hours of cultivation of S. aureus and E. coli (p?S. aureus (p?25 rendered the similar growth and colony size of S. aureus (p?>?0.05), compared with that containing Bacto Peptone. Scanning electron microscopic study revealed no differences in size and shape of microorganisms cultured in HF₂₅ and Bacto Peptone (p?>?0.05).