Many studies have reported the use of near infrared (NIR) spectroscopy to characterize wines or to predict wine chemical composition. However, little is known about the effect of variation in temperature on the NIR spectrum of wine and the subsequent effect on the performance of calibrations used to measure chemical composition. Several parameters influence the spectra of organic molecules in the NIR region, with temperature being one of the most important factors affecting the vibration intensity and frequency of molecular bonds. Wine is a complex mixture of chemical components (e.g. water, sugars, organic acids, and ethanol), and a simple ethanol and water model solution cannot be used to study the possible effects of temperature variations in the NIR spectrum of wine. Ten red and 10 white wines were scanned in triplicate at six different temperatures (25 • C, 30 • C, 35 • C, 40 • C, 45 • C and 50 • C) in the visible (vis) and NIR regions (400-2500 nm) in a monochromator instrument in transmission mode (1 mm path length). Principal component analysis (PCA) and partial least squares (PLS) regression models were developed using full cross validation (leave-one-out). These models were used to interpret the spectra and to develop calibrations for alcohol, sugars (glucose + fructose) and pH at different temperatures. The results showed that differences in the spectra around 970 nm and 1400 nm, related to O H bonding were observed for both varieties. Additionally an effect of temperature on the vis region of red wine spectra was observed. The standard error of cross validation (SECV) achieved for the PLS calibration models tended to inverse as the temperature increased. The practical implication of this study it is recommended that the temperature of scanning for wine analysis using a 1 mm path length cuvette should be between 30 • C and 35 • C.

Spectroscopic techniques offer the potential to simplify and reduce analytical times for a range of grape and wine analytes. It is this aspect, together with the ability to simultaneously measure several analytes, which was the impetus for developing spectroscopic methods. The Australian Wine Research Institute (AWRI) has long used spectroscopic analysis of wines in the ultraviolet (UV) and visible (Vis) wavelengths, and since 1998 has been investigating applications of spectroscopic techniques in the near infrared (NIR) and mid-infrared (MIR) wavelength regions of the electromagnetic spectrum for the rapid analysis and quality control of both grapes and wine by the Australian wine industry. This paper reviews the use of several spectroscopic techniques, including NIR, MIR, and Vis, combined with chemometrics, to assess grape and wine composition in the Australian wine industry. The achievements, current research, and proposed further applications of different spectroscopic techniques are discussed in studies into the assessments of red grape composition and of fungal diseases in grapes, monitoring phenolic compounds during red wine fermentations, quality grading of red, white and fortified wine styles, monitoring wine distillation processes, and yeast strain classification.

The objective of this study was to evaluate the feasibility of using attenuated total reflectance (ATR)-mid infrared (MIR) spectroscopy to predict starch pasting properties, measured using the rapid visco analyser (RVA) method, in barley flour samples, as a potential screening tool in breeding programs.

Two mammalian mitochondrial transcription factors (TFB1M and TFB2M) share homology with universally expressed dimethyltransferases that modify conserved adenines in the small ribosomal subunit rRNA. Work in this issue (Metodiev et al., 2009) shows that loss of TFB1M abolishes mitochondrial ribosome assembly without affecting mitochondrial transcription.

This study reports the combination of multivariate techniques and pollen count analysis to classify honey samples according to botanical sources, in samples from Uruguay. Honey samples from different botanical origens, namely Eucalyptus spp. (n = 10), Lotus spp. (n = 12), Salix spp. (n = 5), "mil flores" (Myrtaceae spp.) (n = 12) and coronilla (Scutia buxifolia Reissek) (n = 10) were analysed using Melissopalynology (pollen identification). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to classify the honey samples accordingly to their botanical origen based on a pollen count. Honey samples of higher percentage (> 70%) of Eucalyptus, Lotus and Scutia pollen were 100% correctly classified, whilst samples from Myrtaceae spp. and Salix were 80 and 66% correctly classified, respectively. The use of PCA and LDA combined with pollen identification proved useful in characterizing honey samples from different botanical origens.

Gold nanoparticles (AuNPs) are excellent tools for cancer cell imaging and basic research. However, they have yet to reach their full potential in the clinic. At present, we are only beginning to understand the molecular mechanisms that underlie the biological effects of AuNPs, including the structural and functional changes of cancer cells. This knowledge is critical for two aspects of nanomedicine. First, it will define the AuNP-induced events at the subcellular and molecular level, thereby possibly identifying new targets for cancer treatment. Second, it could provide new strategies to improve AuNP-dependent cancer diagnosis and treatment. Our review summarizes the impact of AuNPs on selected subcellular organelles that are relevant to cancer therapy. We focus on the nucleus, its subcompartments, and mitochondria, because they are intimately linked to cancer cell survival, growth, proliferation and death. While non-targeted AuNPs can damage tumor cells, concentrating AuNPs in particular subcellular locations will likely improve tumor cell killing. Thus, it will increase cancer cell damage by photothermal ablation, mechanical injury or localized drug delivery. This concept is promising, but AuNPs have to overcome multiple hurdles to perform these tasks. AuNP size, morphology and surface modification are critical parameters for their delivery to organelles. Recent strategies explored all of these variables, and surface functionalization has become crucial to concentrate AuNPs in subcellular compartments. Here, we highlight the use of AuNPs to damage cancer cells and their organelles. We discuss current limitations of AuNP-based cancer research and conclude with future directions for AuNP-dependent cancer treatment.

The objective of this study was to explore the use of near infrared (NIR) spectroscopy and chemometrics to monitor the degree of heat treatment of fish meal. Six batches of fish meal (approximately 500 g) were split in sub-samples of 50 g and heated at constant temperature (60 ± 5°C) for different periods of time (15 and 30 min, 1, 2, 3, 4, 5, 6, 8, 48 and 72 h) in a force air oven and scanned in the NIR region (1100-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), stepwise discriminant analysis (SLDA) and partial least square regression (PLS) models were used to interpret, classify and predict the extent to heat treatment in fish meal samples. The SLDA models correctly classified 80% and 100% of fish meal samples belonging to the untreated fish meal and after 4, 5 and 6 h of heat treatment. However, samples heated for 30 min, 1, 2 and 3 h yield poor classification rates (less than 50%). This study demonstrated the potential ability of NIR spectroscopy to predict and classify the extent of heat treatment during the production of fish meal. However, further research must carry out in order to validate the NIR calibrations to predict the degree of heat treatment in fish meal expose to a shorter time.

Real-world systems are usually multivariate and hence usually cannot be adequately described by one selected variable without the risk of serious misrepresentation. Analyzing the effect of one variable at a time by analysis of variance techniques can give useful descriptive information, but this will not give specifi c information about relationships among variables and other important relationships in the entire matrix. Multivariate data analysis was developed in the late 1960s, and used by a number of research groups in analytical and physical organic chemistry due to the introduction of instrumentation giving multivariate responses for each sample analyzed. Development of such methods was also made possible by the availability of computers. Multivariate data analysis involves the use of mathematical and statistical techniques to extract information from complex data sets. The objective of this paper is to briefl y describe and illustrate some multivariate data analysis methods used for grape and wine analysis.

Spectroscopic techniques such as near infrared (NIR) spectroscopy are used in the food industry to monitor and assess the composition and quality of products. Similar to other food industries, the wine industry has a clear need for simple, rapid and cost-effective techniques for objectively evaluating the quality of grapes, wines and spirits. Thirty years have passed since the first work reported by Kaffka and Norris on the use of NIR spectroscopy to analyse wine. Since then, NIR spectroscopy has been used for grape and wine compositional analysis, fermentation monitoring and wine grading. However, the use of NIR spectroscopy in the wine industry is still in its infancy. From the analysis of the scatter information available, it appears that NIR spectroscopy is applied in different steps during the wine production. This review highlights the most recent applications of NIR spectroscopy in the grape and wine industry. Additional information is also provided on the use of mid infrared...

Near-infrared reflectance (NIR) spectroscopy combined with chemometrics was used to assess nitrogen (N) and dry matter content (DM) and chlorophyll in whole-wheat plant (Triticum aestivum L). Whole-wheat plant samples (n = 245) were analysed by reference method and by visible and NIR spectroscopy, in fresh (n = 182) and dry (n = 63) presentations, respectively. Calibration equations were developed using partial least squares (PLS) and validated using full cross-validation (leave-one-out method). Coefficient of determination in calibration (R 2 CAL) and the standard error of cross-validation (SECV) for N content in fresh sample presentation, after second derivative, were 0.89 (SECV: 0.64%), 0.86 (SECV: 0.66%) and 0.82 (SECV: 0.74%) using the visible + NIR, NIR and visible wavelength regions, respectively. Dry sample presentation gave better R 2 CAL and SECV for N compared with fresh presentation (R 2 CAL > 0.90, SECV <0.20%) using visible + NIR. The results demonstrated that NIR is a suitable method to assess N concentration in wheat plant using fresh samples (unground and undried).

This study reports both the chemical and physical characterization of honey samples produced in Uruguay and the classification of honeys by floral origen using chemometrics. Moisture (M), pH, electric conductivity (EC), hydroxymethyl-2-furaldehyde and colour composition were analysed. As well as composition, classification of honey samples from different floral origens namely pasture (n ¼ 6), Eucalyptus spp. (n ¼ 6), Citrus spp. (n ¼ 5), Baccharis spp. (n ¼ 5), multifloral (n ¼ 2) and others (n ¼ 6) was attempted using principal component analysis and linear discriminant analysis. All the honey samples were found to meet the international specifications for the chemical parameters evaluated. The EC, M and pH were the variables that explain the classification of honey samples according to floral origen. More than 80% of the samples belonging to pasture, Citrus spp. and Baccharis spp. honeys were correctly classified according to its floral origen. Further studies are needed in order to investigate the effect of other floral and geographical (region) to provide a robust model to classify honey samples.

The utilization of chemometric methods in the quantitative and qualitative analysis of feeds, foods, medicine and so on has been accompanied with the great evolution in the progress and in the near infrared spectroscopy (NIRS). Hence, recently the application of NIR spectroscopy has extended on the context of genetics and transgenic products. The aim of this review was to investigate the application of NIR spectroscopy to identificate transgenic products and to compare it with the traditional methods. The results of copious researches showed that the application of NIRS technology was successful to distinguish transgenic foods and it has advantages such as fast, avoiding time-consuming, non-destructive and low cost in relation to the antecedent methods such as PCR and ELISA.

Soil organic matter (SOM) is a complex mixture containing a variety of organic molecular structures. As a consequence, interpretation of the infrared (IR) spectra of SOM is difficult and ambiguous and there is a necessity to establish more reliable IR spectral band assignments. We investigated a novel approach to identify IR spectral bands based on correlation with more easily interpreted nuclear magnetic resonance (NMR) spectra. The IR spectra of HF-treated soils were obtained in both near-and mid-infrared (NIR and MIR) regions and generalized two-dimensional (2D) correlation spectroscopy was employed as the computational correlation tool. 2D NMR/NIR and 2D NMR/MIR heterospectral correlation analyses were performed, separately. We found that NIR spectroscopy could identify aliphatic carbon in SOM as a broad peak occupying the entire NIR region. On the other hand, the MIR spectra contained stronger and more distinct signals than NIR from most of the major carbon types. Bands due to aromatic carbon and carboxyl groups were identified in the regions 4000-3500 cm −1 and 850-500 cm −1 , respectively, and bands due to aliphatic carbon appeared around 3500-2600 cm −1 and 2000-1000 cm −1. Most (but not all) of these assignments are consistent with assignments based on MIR spectra of model compounds. These findings will assist in developing new IR spectroscopy tools for characterizing the chemistry of SOM more accurately and, perhaps, for monitoring its changes more sensitively.

The aim of this work was to explore the potential of visible (Vis) and near infrared reflectance (NIR) spectroscopy to measure quality characteristics in whole grain wheat (Triticum aestivum L.) as a tool in breeding programs. A total of 100 samples were analyzed by the reference methods for crude protein (CP), wet gluten (WG) and sodium dodecyl sulfate (SDS) sedimentation test. Whole grain samples were scanned in a NIR monochromator instrument (400-2500 nm) in reflectance. Partial least squares (PLS) were used to develop calibration equations for the quality characteristics in whole wheat. Calibration models were validated using an independent set of samples (n = 50) randomly selected from the population set. The uncertainty of the PLS models was evaluated by the standard error of prediction (SEP). The SEP obtained were 0.35% for CP, 2.04 for SDS and 4.14% for WG. It was concluded that NIR spectroscopy might be used as a screening tool to segregate early generations of wheat genotypes. At a later stage is needed to improve the accuracy of the NIR calibrations, broadening the calibration spectra with the incorporation of more genotypes and different crop years.

The aim of this work was to explore the potential of visible (Vis) and near infrared reflectance (NIR) spectroscopy to measure quality characteristics in whole grain wheat (Triticum aestivum L.) as a tool in breeding programs. A total of 100 samples were analyzed by the reference methods for crude protein (CP), wet gluten (WG) and sodium dodecyl sulfate (SDS) sedimentation test. Whole grain samples were scanned in a NIR monochromator instrument (400-2500 nm) in reflectance. Partial least squares (PLS) were used to develop calibration equations for the quality characteristics in whole wheat. Calibration models were validated using an independent set of samples (n = 50) randomly selected from the population set. The uncertainty of the PLS models was evaluated by the standard error of prediction (SEP). The SEP obtained were 0.35% for CP, 2.04 for SDS and 4.14% for WG. It was concluded that NIR spectroscopy might be used as a screening tool to segregate early generations of wheat genotypes. At a later stage is needed to improve the accuracy of the NIR calibrations, broadening the calibration spectra with the incorporation of more genotypes and different crop years.

The content of phenolic compounds determines the state of phenolic ripening of red grapes, which is a key criterion in setting the harvest date to produce quality red wines. Wine phenolics are also important quality components that contribute to the color, taste, and mouth feel of wines. Spectroscopic techniques (e.g., near and mid infrared) offer the potential to simplify and reduce the analytical time for a range of grape and wine analytes. It is this characteristic, together with the ability to simultaneously measure several analytes in the same sample at the same time, which makes these techniques very attractive for use in both industry and research. The objective of this mini review is to present examples and to discuss different applications of visible (VIS), near infrared (NIR) and mid infrared (MIR) to assess and measure phenolic compounds in grape and wines.

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