Nondestructive estimation of maturity and textural properties on tomato ‘Momotaro’ by near infrared spectroscopy

Near infrared spectroscopy offers the possibility to classify and predict the internal quality of fruits and vegetables. The objective of this study was to evaluate the ability of near infrared spectroscopy to classify the maturity level and to predict textural properties of tomatoes variety “Momotaro”. Principal component analysis (PCA) and Soft independent modeling of class analogy (SIMCA) were used to distinguish among different maturities (mature green, pink and red). Partial least squares (PLS) regression was used to estimate textural properties, alcohol insoluble solids and soluble solids content of the tomatoes. The PCA calibration model with mean normalization pretreatment spectra of mature green tomatoes, gave the highest distinguishability (96.85%). It could classify 100.00% of red and pink tomatoes. The SIMCA model could not give better accuracy in maturity classification than individual PCA models. Among the textural parameters measured, the bioyield force from the puncture test with the near infrared (NIR) spectra (between 1100 and 1800 nm) pretreated by multiplicative scatter correction (MSC) had the highest correlation coefficient between NIR predicted and reference values (r = 0.95) and lowest standard error of prediction (SEP = 0.35 N) and bias of 0.19 N. The ratio of standard deviation of reference data of prediction set to standard error of prediction (RPD) was 2.71. In the case of Momotaro tomato, NIR spectroscopy by using PLS regression could not predict alcohol insoluble solids in fresh weight accurately but could predict soluble solids content well with r of 0.80, SEP of 0.210 %Brix and bias of 0.022 %Brix.     

Classification of dry-cured hams according to the maturation time using near infrared spectra and artificial neural networks

An attempt to classify dry-cured hams according to the maturation time on the basis of near infrared (NIR) spectra was studied. The study comprised 128 samples of biceps femoris (BF) muscle from dry-cured hams matured for 10 (n = 32), 12 (n = 32), 14 (n = 32) or 16 months (n = 32). Samples were minced and scanned in the wavelength range from 400 to 2500 nm using spectrometer NIR System model 6500 (Silver Spring, MD, USA). Spectral data were used for i) splitting of samples into the training and test set using 2D Kohonen artificial neural networks (ANN) and for ii) construction of classification models using counter-propagation ANN (CP-ANN). Different models were tested, and the one selected was based on the lowest percentage of misclassified test samples (external validation). Overall correctness of the classification was 79.7%, which demonstrates practical relevance of using NIR spectroscopy and ANN for dry-cured ham processing control.     

Visible and near-infrared spectroscopic analysis of raw milk for cow health monitoring: reflectance or transmittance?

The composition of produced milk has great value for the dairy farmer. It determines the economic value of the milk and provides valuable information about the metabolism of the corresponding cow. Therefore, online measurement of milk components during milking 2 or more times per day would provide knowledge about the current health and nutritional status of each cow individually. This information provides a solid basis for optimizing cow management. The potential of visible and near-infrared (Vis/NIR) spectroscopy for predicting the fat, crude protein, lactose, and urea content of raw milk online during milking was, therefore, investigated in this study. Two measurement modes (reflectance and transmittance) and different wavelength ranges for Vis/NIR spectroscopy were evaluated and their ability to measure the milk composition online was compared. The Vis/NIR reflectance measurements allowed for very accurate monitoring of the fat and crude protein content in raw milk (R² > 0.95), but resulted in poor lactose predictions (R² < 0.75). In contrast, Vis/NIR transmittance spectra of the milk samples gave accurate fat and crude protein predictions (R² > 0.90) and useful lactose predictions (R² = 0.88). Neither Vis/NIR reflectance nor transmittance spectroscopy lead to an acceptable prediction of the milk urea content. Transmittance spectroscopy can thus be used to predict the 3 major milk components, but with lower accuracy for fat and crude protein than the reflectance mode. Moreover, the small sample thickness (1 mm) required for NIR transmittance measurement considerably complicates its online use.     

Near infrared spectroscopy as an on-line method to quantitatively determine glycogen and predict ultimate pH in pre rigor bovine M. longissimus dorsi

The potential of near infrared (NIR) spectroscopy as an on-line method to quantify glycogen and predict ultimate pH (pH_u) of pre rigor beef M. longissimus dorsi (LD) was assessed. NIR spectra (538 to 1677 nm) of pre rigor LD from steers, cows and bulls were collected early post mortem and measurements were made for pre rigor glycogen concentration and pH_u. Spectral and measured data were combined to develop models to quantify glycogen and predict the pH_u of pre rigor LD. NIR spectra and pre rigor predicted values obtained from quantitative models were shown to be poorly correlated against glycogen and pH_u (r² = 0.23 and 0.20, respectively). Qualitative models developed to categorise each muscle according to their pH_u were able to correctly categorise 42% of high pH_u samples. Optimum qualitative and quantitative models derived from NIR spectra found low correlation between predicted values and reference measurements.     

Monitoring water uptake in whole barley (Hordeum vulgare L.) grain during steeping using near infrared reflectance spectroscopy

The aim of this study was to evaluate the use of near infrared reflectance (NIR) spectroscopy to monitor water uptake and steeping time in whole barley samples as a rapid and easy to use technique. Whole barley grain samples were steeped in water, and subsamples were analyzed for water uptake (gravimetric method) and using NIR spectroscopy. The spectra and the analytical data were used to develop partial least squares (PLS) calibrations to predict water uptake and steeping time. Cross validation models for water uptake and steeping time gave a coefficient of determination in cross validation (R²) and the standard error of prediction (SEP) of 0.90 (SEP = 5.36 g/100 g fw) and 0.92 (SEP = 3.93 h), respectively. This study showed that NIR spectroscopy combined with PLS regression showed promise as a rapid, non-destructive method to monitor and measure water uptake and steeping time in whole barley during soaking.     

Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics

Near-infrared (NIR) spectroscopy combined with chemometrics methods has been used to detect adulteration of honey samples. The sample set contained 135 spectra of authentic (n = 68) and adulterated (n = 67) honey samples. Spectral data were compressed using wavelet transformation (WT) and principal component analysis (PCA), respectively. In this paper, five classification modeling methods including least square support vector machine (LS-SVM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), linear discriminant analysis (LDA), and K-nearest neighbors (KNN) were adopted to correctly classify pure and adulterated honey samples. WT proved more effective than PCA, as a means for variables selection. Best classification models were achieved with LS-SVM. A total accuracy of 95.1% and the area under the receiver operating characteristic curves (AUC) of 0.952 for test set were obtained by LS-SVM. The results showed that WT-LS-SVM can be as a rapid screening technique for detection of this type of honey adulteration with good accuracy and better generalization.     

Preliminary study on the application of visible–near infrared spectroscopy and chemometrics to classify Riesling wines from different countries

Visible (VIS) and near infrared (NIR) spectroscopy combined with chemometrics was used in an attempt to classify commercial Riesling wines from different countries (Australia, New Zealand, France and Germany). Commercial Riesling wines (n = 50) were scanned in the VIS and NIR regions (400–2500 nm) in a monochromator instrument, in transmission mode. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and stepwise linear discriminant analysis (SLDA) based on PCA scores were used to classify Riesling wines according to their country of origin. Full cross validation (leave-one-out) was used as the validation method when classification models were developed. PLS-DA models correctly classified 97.5%, 80% and 70.5% of the Australian, New Zealand and European (France and Germany) Riesling wines, respectively. SLDA calibration models correctly classified 86%, 67%, 67% and 87.5% of the Australian, New Zealand, French and German Riesling wines, respectively. These results demonstrated that the VIS and NIR spectra contain information that when used with chemometrics allow discrimination between wines from different countries. To further validate the ability of VIS–NIR to classify white wine samples, a larger sample set will be required.     

The relevance of different near infrared technologies and sample treatments for predicting meat quality traits in commercial beef cuts

Visible and near infrared reflectance (Vis-NIR, 350 to 1800 nm), and near infrared transmittance (NIT, 850 to 1050 nm) spectroscopy were used to predict beef quality traits of intact and ground meat samples. Calibration equations were developed from reference data (n = 312) of pH, color traits (L*, a*, and b*), ageing loss (%), cooking loss (%), and Warner–Bratzler shear force (WBSF, N) using partial least squares regressions. Predictive ability of the models was assessed by coefficient of determination of cross-validation (R²_CV) and root mean square error of cross-validation. Quality traits were better predicted on intact than on ground samples, and the best results were obtained using Vis-NIR spectroscopy. Predictions were good (R²_CV = 0.62 to 0.73) for pH, L*, and a*, hardly sufficient (R²_CV = 0.34 to 0.60) for b*, cooking loss, and WBSF, and unsatisfactory for ageing loss (R²_CV = 0.15). Vis-NIR spectroscopy might be used to predict some physical beef quality traits on intact meat samples.     

Near-infrared spectroscopic analysis of macronutrients and energy in homogenized meals

Near-infrared (NIR) reflectance spectroscopy was evaluated as a rapid and environmentally benign technique for the simultaneous determination of macronutrients and energy in commercially available, packaged meals. Reflectance spectra (400–2498 nm) of homogenized meals were obtained with a dispersive NIR spectrometer. Protein and moisture were measured by AOAC reference methods, total fat by a semi-automated acid hydrolysis, solvent extraction, gravimetric method and total carbohydrate calculated. Energy was calculated using Atwater factors. Using multivariate analysis software, PLS models (n = 113–115 products) were developed to relate NIR spectra of homogenized meals to the corresponding reference values. The models predicted components and energy in validation samples (n = 37–38 products), overall, with r² of above 0.96. Ratios of deviation to performance were between 3.6 and 6.6, and indicated adequacy of the models for screening, quality control, or process control. Performance of the models varied substantially when used to predict sub-groups of meals within the validation set.     

On-line prediction of fresh pork quality using visible/near-infrared reflectance spectroscopy

Visible/near-infrared (Vis/NIR) spectroscopy was tested to predict the quality attributes of fresh pork (content of intramuscular fat, protein and water, pH and shear force value) on-line. Vis/NIR spectra (350–1100 nm) were obtained from 211 samples using a prototype. Partial least-squares regression (PLSR) models were developed by external validation with wavelet de-noising and several pre-processing methods. The 6th order Daubechies wavelet with 6 decomposition levels (db6–6) showed high de-noising ability with good information preservation. The first derivative of db6–6 de-noised spectra combined with multiplicative scatter correction yielded the prediction models with the highest coefficient of determination (R²) for all traits in both calibration and validation periods, which were all above 0.757 except for the prediction of shear force value. The results indicate that Vis/NIR spectroscopy is a promising technique to roughly predict the quality attributes of intact fresh pork on-line.     

On-line prediction of pH values in fresh pork using visible/near-infrared spectroscopy with wavelet de-noising and variable selection methods

The visible/near-infrared (Vis/NIR) reflectance spectroscopy as an on-line approach to assess the pH value in fresh pork was investigated. Multivariate calibration was carried out by using chemometrics. Discrete wavelet transform was applied to de-noise the spectra scanned on-line, and several variable selection methods were proposed to simplify the calibration models. The study found that the model based on the spectra de-noised by Daubechies 6 wavelet (db6) at decomposition level 6, soft thresholding strategy and minimaxi threshold estimator gave reasonable performance (r > 0.900, root mean square error of calibration (RMSEC) = 0.100, cross validation (RMSECV) = 0.139 and prediction (RMSEP) = 0.125). Then, only 15% variables from this model were selected via the method of uninformative variable elimination to develop a simpler model, of which the performance deterioration could be ignored. The results showed that Vis/NIR can be used to predict pH value in fresh pork on-line, and variable selection can provide a simpler, more cost-effective calibration model.     

Determination of Chinese honey adulterated with high fructose corn syrup by near infrared spectroscopy

The use of fibre optic diffuse reflectance near infrared spectroscopy (NIR) in combination with chemometric techniques has been investigated to discriminate authenticity of honey. NIR spectra of unadulterated honey and adulterated honey samples with high fructose corn syrup were registered within 10,000–4000 cm⁻¹ spectral region. Discriminant partial least squares (DPLS) models were constructed to distinguish between unadulterated honey and adulterated honey samples and main bands responsible for the discrimination of samples are in the range of 6000–10,000 cm⁻¹. For these models, the correct classification rate for calibration samples were above 90%. Hundred percentage of unadulterated honey and 95% of adulterated honey samples from test set were correctly classified after appropriate preprocessing of first derivative, 13 smoothing points, followed by mean centering pre-treatment and eight model factors, respectively. Our results showed that NIR spectroscopy data with chemometrics techniques can be applied to rapid detecting honey adulteration with high fructose corn syrup.     

Optimisation of ethanol modified supercritical carbon dioxide on the extract yield and antioxidant activity from Garcinia mangostana L.

Xanthones are an important class of secondary metabolites present in mangosteen fruit pericarp. Herein we have used supercritical fluid technology to extract the active constituents from mangosteen pericarp. Ethanol was added (5% w/w) as a co-solvent to increase the polarity of the CO₂, thus favouring the extraction of xanthones. The maximum extraction yield of 15 wt.% was achieved at a pressure of 280 bar, temperature of 50 °C and a time of 8 h, while without co-solvent the yield was 7.5 wt.%. Conditions for antioxidant activity as measured by 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical-scavenging assay was 280 bar at 50 °C and 5 h. Box–Behnken design was used to study the efficiency of extraction pressure (180, 280, and 380 bar), temperature (40, 50, and 60 °C) and time (2–8 h) on the total extraction yields and their radical scavenging activity. Experimental results of the total extract yield and radical scavenging activity were close to the predicted values calculated from the polynomial response surface models equations (R² = 0.99 and 0.98).     

First steps towards the development of a non-destructive technique for the quality control of wine grapes during on-vine ripening and on arrival at the winery

NIR spectroscopy was used as a non-destructive technique for the assessment of changes in certain internal quality properties of wine grapes (Vitis vinifera L.) during on-vine ripening and at harvest. A total of 108 different wine grape samples were used to construct calibration models based on reference data and NIR spectral data, obtained using a commercially-available diode-array spectrophotometer (380-1700 nm). The feasibility of testing bunches of intact grapes was investigated and compared with more traditional methods of presentation, such as berries or must. Predictive models were constructed to quantify changes in soluble solid content (SSC, °Brix), reducing-sugar content (g/l), pH-value, titrable acidity (g/l tartaric acid), tartaric acid (g/l) and malic acid (g/l), these being the major parameters used to chart ripening. NIRS technology provided good precision for the bunch analysis mode assayed for SSC (r² = 0.89; SECV = 1.41 °Brix), for reducing-sugar content (r² = 0.87; SECV = 17.13 g/l) and for pH-value (r² = 0.69; SECV = 0.19). Models developed for testing other fruit acidity parameters yielded results sufficient to provide a screening tool to distinguish between low and high acidity values in intact grapes. Significantly, the results obtained with bunch presentation were similar to those obtained with berries and must, thus justifying further implementation of NIRS technology for the non-destructive analysis of quality properties both during on-vine ripening and on arrival at the winery. This method allows musts to be processed separately depending on initial grape quality, assessed with a single spectrum measurement and in a matter of seconds.     

Miniature handheld NIR sensor for the on-site non-destructive assessment of post-harvest quality and refrigerated storage behavior in plums

This study evaluated the feasibility of using a handheld micro-electro-mechanical system (MEMS) spectrometer working in the 1600–2400 nm range for the measurement of quality-related parameters (soluble solid content, firmness, variety and post-harvest storage duration under refrigeration) in intact plums. Spectroscopic measurements were also made for each fruit using a diode-array Vis–NIR spectrophotometer (400–1700 nm) for purposes of comparison. A total of 264 plums (Prunus salicina L.) cv. ‘Black Diamond’, ‘Golden Globe’, ‘Golden Japan’, ‘Fortune’, ‘Friar’ and ‘Santa Rosa’, received and stored at 0 °C and 95% RH for 9 days, were used to build calibration models using different spectral signal pre-treatments and the modified partial least squares regression method. The two NIR instruments evaluated provided good precision, although the diode-array instrument yielded slightly greater precision for soluble solid content; statistic values were r² = 0.73 and the standard error of cross validation (SECV) = 1.11% for calibration, and r² = 0.68 and the standard error of prediction (SEP) = 1.22% for validation. Firmness measurements were less precise in both instruments, though again slightly better in the diode-array instrument: r² = 0.64 and SECV = 1.77 N for calibration; and r² = 0.61 and SEP = 2.30 N for validation, respectively. The performance of the two instruments for classifying plums by variety and by refrigerated post-harvest storage duration (0, 6 and 9 days) was evaluated using partial least square-discriminant analysis. A total of 96.5 % of samples were correctly assigned to their variety, while 94.5 % of plums were correctly assigned to their refrigerated storage day. In general, promising results were obtained with both instruments, with similar levels of accuracy for the measurements for soluble solid content, variety and refrigerated storage duration; the prediction model developed using the diode-array spectrophotometer provided better results for firmness.     

Rapid Non-destructive Detection of Spoilage of Intact Chicken Breast Muscle Using Near-infrared and Fourier Transform Mid-infrared Spectroscopy and Multivariate Statistics

Near-infrared (NIR) transflectance and Fourier transform-infrared (FT-IR) attenuated total reflectance spectra of intact chicken breast muscle packed under aerobic conditions and stored at 4° for 14 days were collected and investigated for their potential use in rapid non-destructive detection of spoilage. Multiplicative scatter correction-transformed NIR and standard normal variate-transformed FT-IR spectra were analysed using principal component analysis (PCA), partial least-squares discriminant analysis (PLS2-DA) and outer product analysis (OPA). PCA and PLS2-DA regression failed to completely discriminate between days 0 and 4 samples (total viable count (TVC) days 0 and 4 = 5.23 and 6.75 log₁₀ cfu g⁻¹) which had bacterial loads smaller than the accepted levels (8 log₁₀ cfu g⁻¹) of sensory spoilage detection but classified correctly days 8 and 14 samples (TVC days 8 and 14 = 9.61 and 10.37 log₁₀ cfu g⁻¹). OPA performed on both NIR and FT-IR datasets revealed several correlations that highlight the effect of proteolysis in influencing the spectra. These correlations indicate that increase in free amino acids and peptides could be the main factor in the discrimination of intact chicken breast muscle. This investigation suggests that NIR and FT-IR spectroscopy can become useful, rapid, non-destructive tools for spoilage detection.     

Assessment of maple syrup physico-chemistry and typicity by means of fluorescence spectroscopy

Maple syrup is a natural sweetener obtained from the transformation of maple sap collected mostly from sugar maple (Acer saccharum Marsh) in North America. At present, simple physico-chemical tests are used for routine quality control. Inspectors also taste all batches on the market to ensure authenticity. Because of the presence of various aromatic compounds in sap and syrup, intrinsic fluorescence was tested as a means to characterize the physico-chemistry and typicity of maple syrup. Two hundred samples of sap and their corresponding syrup were obtained from various farms in 2003 and 2004. They were analysed by conventional physico-chemical tests and by fluorescence spectroscopy. Two major regions of fluorescence were found, which were mostly the same for sap and syrup. The first one was at 320 nm, excited at 275 nm, and the second one at 460 nm, excited at 360 (syrup) or 370 nm (sap). The first peak diminishes as harvesting season progresses, while the second peak increases, making it possible to predict the harvesting period of syrup from its spectra (r² = 0.88 in 2003 and 0.81 in 2004). Color of syrup (r² = 0.91 and 0.88) and bacterial counts in sap (r² = 0.75 and 0.78) were also predicted from syrup spectra. Results show that sap spectra are related to syrup spectra and could potentially be used as predictor of quality prior to transformation. Discriminant analysis revealed that between 71% and 95% of syrup samples were correctly classified according to the farm of origin in 2003, and between 78% and 100% in 2004. Proximity was not always a factor of explanation of misclassification, suggesting that precise farm location, rather than the broad region of production is the major factor of typicity.     

Mid infrared spectroscopy and multivariate analysis: A tool to discriminate between organic and non-organic wines grown in Australia

The combination of mid infrared (MIR) spectroscopy and multivariate analysis was explored as a tool to classify commercial wines sourced from organic (ORG) and non-organic (NORG) production systems. Commercial ORG (n = 57) and NORG (n = 115) red and white wine samples from 13 growing regions in Australia were analysed using a MIR spectrophotometer. Discriminant models based on MIR spectra were developed using principal component analysis (PCA), discriminant partial least squares (DPLS) regression and linear discriminant analysis (LDA). Overall, the LDA models based on the PCA scores correctly classified on average, more than 75% of the wine samples while the DPLS models correctly classified more than 85% of the wines belonging to ORG and NORG production systems, respectively. These results showed that MIR combined with discriminant techniques might be a suitable method that can be easily implemented by the wine industry to classify wines produced under organic systems.     

Monitoring the molecular changes by front face fluorescence spectroscopy throughout ripening of a semi-hard cheese

Twenty four semi-hard cheeses produced during autumn (n = 12) and summer (n = 12) periods were manufactured and ripened at an industrial scale. Tryptophan and vitamin A fluorescence spectra were scanned on the 24 cheeses at 2, 30 and 60 days of ripening. Principal component analysis (PCA) and factorial discriminant analysis (FDA) were applied on the spectral data sets. The first five principal components (PCs) of the PCA extracted from each data set (tryptophan or vitamin A) of cheeses produced during autumn or summer period were pooled into a single matrix and analysed by FDA. Regarding cheeses produced during the autumn period, the percentage of samples correctly classified was 95.8% and 86.1% for the calibration and validation samples, respectively. Similar results were obtained from cheeses produced during the summer period. Finally, concatenation technique was applied to the tryptophan and vitamin A spectra recorded on cheeses independently of their production seasons. Correct classification was observed for 87.5% and 80.6% for the calibration and validation samples, respectively. Although this statistical technique did not allow 100% correct classification for all groups, the results obtained were promising considering the significant effect of the season on the cheese characteristics.     

Monitoring quality loss of pasteurized skim milk using visible and short wavelength near-infrared spectroscopy and multivariate analysis

Visible and short wavelength near-infrared diffuse reflectance spectroscopy (600 to 1,100 nm) was evaluated as a technique for detecting and monitoring spoilage of pasteurized skim milk at 3 storage temperatures (6, 21, and 37°C) over 3 to 30 h (control, t = 0 h; n = 3). Spectra, total aerobic plate count, and pH were obtained, with a total of 60 spectra acquired per sample. Multivariate statistical procedures, including principal component analysis, soft independent modeling of class analogy, and partial least squares calibration models were developed for predicting the degree of milk spoilage. Principal component analysis showed apparent clustering and segregation of milk samples that were stored at different time intervals. Milk samples that were stored for 30 h or less at different temperatures were noticeably separated from control and distinctly clustered. Soft independent modeling of class analogy analysis could correctly classify 88 to 93% of spectra of incubated samples from control at 30 h. A partial least squares model with 5 latent variables correlating spectral features with bacterial counts and pH yielded a correlation coefficient (R = 0.99 and 0.99) and a standard error of prediction (0.34 log₁₀ cfu/mL and 0.031 pH unit), respectively. It may be feasible to use short wavelength near-infrared spectroscopy to detect and monitor milk spoilage rapidly and noninvasively by correlating changes in spectral features with the level of bacterial proliferation and milk spoilage.