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The plate count method is commonly used to detect the total viable count (TVC) of bacteria in pork, which is timeconsuming and destructive. It has also been used to study the changes of the TVC in pork under different storage conditions. In recent years, many scholars have explored the non-destructive methods on detecting TVC by using visible near infrared (VIS/NIR) technology and hyperspectral technology. The TVC in chilled pork was monitored under high oxygen condition in this study by using hyperspectral technology in order to evaluate the changes of total bacterial count during storage, and then evaluate advantages and disadvantages of the storage condition. The VIS/NIR hyperspectral images of samples stored in high oxygen condition was acquired by a hyperspectral system in range of 400~1100nm. The actual reference value of total bacteria was measured by standard plate count method, and the results were obtained in 48 hours. The reflection spectra of the samples are extracted and used for the establishment of prediction model for TVC. The spectral preprocessing methods of standard normal variate transformation (SNV), multiple scatter correction (MSC) and derivation was conducted to the original reflectance spectra of samples. Partial least squares regression (PLSR) of TVC was performed and optimized to be the prediction model. The results show that the near infrared hyperspectral technology based on 400-1100nm combined with PLSR model can describe the growth pattern of the total bacteria count of the chilled pork under the condition of high oxygen very vividly and rapidly. The results obtained in this study demonstrate that the nondestructive method of TVC based on NIR hyperspectral has great potential in monitoring of edible safety in processing and storage of meat.

This work was conducted to determine if a useful model could be developed for a portable visible/near-infrared spectrometer for non-destructively predicting dry matter content in ‘Lapins’ and other cultivars of sweet cherries. Absorptions at a range of 948–957 nm (default selected by the instrument model-building program) or 858–1008 nm (user selected) were used to create models associating absorption values with actual measured dry matter contents of cherries. The best model was created using the user selected waveband and this model was determined to be highly predictive of dry matter, with a resolution of 0.5% dry matter content. External validation of this model was carried out using three different sweet cherry cultivars (Staccato™, Sentennial™, and Sovereign™) and the model was found to be robust, i.e., quite accurate in predicting dry matter content in these other cultivars, with R2 values of 0.96, 0.94, and 0.99, and root mean square error of prediction values of 0.51, 0.74, and 0.56, respectively...

International audience; The potential of multispectral VIS-NIR imaging to discriminate beef meat muscles in relation with their type and animal origin was examined in the present study. Two hundred and forty muscles of three types (Longissimus thoracis, Biceps femoris and Semimembranosus) were obtained from the carcasses of three types of animals, two late-maturing cattle types of animals (Limousin and Blond d'Aquitaine) that grow slowly and deposit more muscles and less fat, compared to one early-maturing cattle types of animals (Angus) which tends to have muscles richer in collagen and in intramuscular fat. Two hundred and forty cube images were collected with nineteen emitting LEDs (405 to 1050 nm) using the Videometer Lab2 device. The image cubes were processed in order to extract image mean spectra and image shape features from co-occurrence and difference of histogram matrices. The results of the PLSDA performed on image texture features and spectral data show a maximum ranging from 63.5 to 83% of good classification depending on the muscle and breed considered. This study demonstrated the promising potential of the VIS-NIR multispectral imager to characterize beef meat muscles based on muscle type and its animal origin.

Er+3 (üç değerlikli erbiyum iyonu) katkılı baryum tantalat fosforları katı hal reaksiyon metoduyla 1425 °C ’de 20 saat sinterlenerek üretilmiştir. Üretilen numuneler X–ışınları difraktometresi (XRD), taramalı elektron mikroskobu (SEM), enerji dağılımı spektroskopisi (EDS) ve görünür–lazer fotoluminesans (FL) analizleri ile karakterize edilmiştir. XRD analizinde, Er+3 katkılı BaTa2O6 yapısı %10 mol oranına kadar tek fazlı yapı özelliğini sürdürürken, %10 mol üzeri seviyelerde az oranda ErTaO4 fazı ortaya çıkmıştır. SEM analizlerinde Er+3 katkılı BaTa2O6 tanelerin boyutu katkı artışına bağlı olarak küçülme göstermiştir. Görünür FL analizlerinde, BaTa2O6 :Er+3 fosforunun emisyon spektrumları ise 500 ile 690 nm dalgaboyları arasında olup, Er+3 iyonuna ait karakteristik emisyon pikleri gözlenmiştir. Ayrıca, Er+3 katkı artışına bağlı olarak emisyon azalması veya konsantrasyon sönümlemesi meydana gelmemiştir. BaTa2O6 :Er+3 fosforlarının Uluslararası Aydınlatma Komisyonu (CIE) renklilik kordinatları diyagramında sarımsı-yeşil bölgede bulunmuştur. Yakın kızılötesi FL analizinde BaTa2O6 :Er+3 fosforunun emisyon spektrumları 1450 ile 1650 nm arasında gözlemlenmiştir. Er3+ (trivalent erbium ion) doped barium tantalate phosphors were produced by solid state reaction method at 1425 C for 20 hrs. The synthesized samples were characterized by X–ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and visible–laser photoluminescence (PL) analyses. In XRD analysis, while Er3+ doped BaTa2O6 structure maintained single phase structure up to 10 mol%, small amount ErTaO4 phase appeared doping levels above 10 mol%. SEM analysis showed that size of Er3+ doped BaTa2O6 grains decreased with increasing doping concentration. In the visible PL analysis, the emission spectra of BaTa2O6 :Er3+ phosphor were between 500 and 690 nm wavelengths, and characteristic emission peaks of Er3+ ion were observed. Furthermore, depending on the increase in Er3+ doping concentration, no emission decreasing or concentration quenching has occurred. The Commission Internationale d’Eclairage (CIE) chromaticity coordinates of the BaTa2O6 :Er3+ phosphors were found to be in the yellowish green region of the chromaticity diagram. In the near infrared PL analysis, the emission spectra of BaTa2O6 :Er3+ phosphors were observed between 1450 and 1650 nm.

A ultra-broadband polarization-independent nearly perfect absorber at visible- near infrared frequencies has been proposed. The absorber bases on a metal-dielectric-metal (MDM) structure. Extraordinary optical absorption with absorption peaks up to 99% can be achieved.

Carrot is a root crop that has relatively high content of vitamin C and carotenoids. Carrot is usually consumed fresh, juiced or cooked. It is also a major source of industrial natural carotenoids, a precursor to vitamin A. Rapid determination of quality parameters indeed helps process engineers and storage managers to track the condition of root crops such as carrots during storage and/or before processing. The objectives of this study were to apply VIS-NIR spectroscopy to noninvasively assess and predict the various quality attributes of carrot (cv. Nectar) including color parameters (L*a*b*), moisture content (MC), total soluble solids (TSS), firmness, vitamin C content, and I²-carotene. Two spectroscopic sensors with different range were used (400-1000 nm and 900-1700 nm). Samples included whole roots and sliced discs. The best prediction models using partial least squares regression (PLSR) yielded correlation coefficient, r and ratio of performance to deviation or r(RPD) with values of 0.92 (2.46), 0.85(1.65), 0.94(2.85), 0.96(3.55), 1.00(17.92), 0.90(2.16), 0.98(4.73), and 0.97(3.88) for L*, a*, b*, firmness, MC, TSS, vitamin C, and I²-carotene, respectively. Generally, whole roots and VIS-NIR sensor have high prediction (r = 0.69 – 1.00). Results obtained in this research could be enhanced with more samples to develop a better and higher accuracy predictive multispectral model for carrot quality prediction with possible extension to other similar vegetables in post-harvest losses reduction.

Visible/near-infrared (Vis/NIR) spectroscopy was applied to determine the ripeness of melons (Cucumis melo L.). The total soluble solids (TSS) and flesh color distribution around the equator fracture surface was studied, and a high correlation between TSS and flesh color in the outer mesocarp region was observed. A new ripeness index, defined as the edible ratio (ER), was proposed. One hundred and twenty melons with similar sizes were selected as samples and divided into a calibration set and a prediction set. Spectra of melons were acquired separately under transmittance and interactance modes. A machine vision system was established to acquire images of the melon fracture surfaces to further calculate the ER values of the melons. Partial least squares (PLS) calibration models were developed to predict the melon ripeness indices, including TSS, surface color (SC), and ER value. The results were promising for TSS, Hunter a, and ER value. For Hunter b, the results were insufficient. In addition, by comparing the performances of the calibration models developed under different detection modes, it was concluded that the best calibration results for TSS and ER were achieved under the transmittance mode, while for Hunter a, the best result was obtained using interactance spectra.

VIS/NIR spectroscopic systems have been extensively and successfully applied on quality assurance for fruits, vegetables, and food products. This study uniquely focused on the efficacy of selected wavelengths on predicting glucose and sucrose for potato tubers of Frito Lay 1879 (FL) and Russet Norkotah (RN) cultivars, and in turn the potential of classification of potatoes based on sugar levels important to the frying industry. Whole tubers, as well as 12.7 mm slices, were scanned using a VIS/NIR interactance spectroscopic system (446–1,125 nm). Interval partial least squares, and genetic algorithm were utilized for wavelength selection. Partial least squares regression (PLSR), and artificial neural networks were applied for building prediction models. Results showed that R(RPD) [correlation coefficient (ratio of reference standard deviation to root mean square error of the model)] for prediction models of glucose were as high as 0.78 (1.61), and 0.95 (3.02) for FL, and RN for slice samples, and 0.81 (1.72), and 0.97 (3.89) for FL and RN respectively in the case of whole tubers. For sucrose models, R(RPD) values were 0.71 (1.43), and 0.78 (1.57) for FL and RN for slice samples, and 0.80 (1.64), and 0.94 (2.82) for whole tubers. Classification of potatoes based on sugar levels was conducted and training models were built and validated using linear discriminant analysis, K-nearest neighbor, partial least squares discriminant analysis, and classifier fusion. Classification errors of the testing set for whole tubers, based on glucose, were as low as 18 and 0 % for FL and RN. For sliced samples, the errors were 16 and 13 % for FL and RN. Generally, higher classification errors were obtained based on sucrose with values of whole tubers as low as 26 and 14 % for FL and RN, and for sliced samples the errors were 23 and 18 % which follows a similar trend as PLSR results. This study presents a potential of using selected wavelengths in the VIS/NIR interactance range of 446–1,125 nm to effectively predict sugars and classify potatoes based on thresholds that are crucial for the frying industry.

Near-infrared (NIR) transmittance spectroscopy, visible/NIR interactance spectroscopy, and visible/NIR hyperspectral imaging modes were used to determine the potential for rapid electronic estimation of glucose, sucrose, specific gravity, primordial leaf count, and soluble solids of (FL) (chipping) and Russet Norkotah (RN) (table) potato cultivars. Whole tubers and 0.5 inch (12.54 mm) slices were evaluated. Partial least squares regression (PLSR) was used to obtain the prediction models. Models for leaf counts and glucose were promising for all modes with the optimum model obtained for leaf count from interactance with sliced samples resulting in R (RPD) values of 0.95(3.29) for FL, and 0.90(2.19) for RN. For glucose, interactance also yielded the best model with R (RPD) values of 0.90(2.14) for FL, and 0.95(3.12) for RN. Models of other constituents were inconsistent between systems. Interactance mode with sliced samples showed best performance for soluble solids for FL with R (RPD) values of 0.55(1.18). Also, interactance mode demonstrated optimal performance for sucrose estimation with R (RPD) values of 0.81(1.63) for FL obtained from sliced samples, and 0.81(1.64) from whole tubers. Transmittance mode with sliced samples showed the best prediction models for RN with R (RPD) values of 0.63(1.30) for sucrose. Finally, specific gravity under interactance and transmittance modes with sliced samples for FL and RN respectively yielded R (RPD) values of 0.61(1.27), and 0.59(1.22).

Crucial physio-chemical changes occuring in eggs during storage after laying lead to loss of egg freshness. In this research, a new method for prediction of egg freshness using transmission visible near infrared spectroscopy was investigated. For this purpose 300 eggs were stored at two control conditions: refrigerator (4–5°C, 75%RH) and room (24–25°C, 40%RH) then by special egg holder, transmission spectroscopy was measured. For two eggs groups, 25 eggs in each group, in six days were tested by spectroscopy, after that Haugh unit and air cell height was measured directly. The non-destructive visible near infrared spectroscopy spectral measurements from 300 to 1100 nm (832 length of wave) were done as well as Haugh unit, air cell height for each egg and created the database for both environments. Finally a maximum likelihood latent root regression algorithm was developed to predict Haugh unit and air cell height by spectrum observation. The database was randomly divided into two parts. Training data, was ...

Silver nanoparticles (NPs) with diameters of 5-10 nm were synthesized by using formaldehyde (HCHO) to reduce silver nitrate (AgNO3) and polyethylenemine (PEI) as a stabilizer. It was found that on the carbon-supported cupper grid (Cu/polyvinylformal (PVF)/C) and under a visible-near-infrared (VIS-NIR, 400-860 nm) lamp illuminating at about 75 degrees C for six minutes, the spherical Ag NPs self-assembled into bigger NPs, triangular nanoprisms, or structures like clock hands. On the glass slides, the NPs firstly self-assembled into spherical particles with 3-6 microm in sizes, then assembled into chain structures comprised of 3-20 spherical particles after being illuminated by the VIS-NIR lamp for about nine minutes. The violent thermal motion of the NPs after being illuminated by the VIS-NIR lamp leads to the self-assembly and the shapes of the self-assembled particles are related to the interactions between the substrates and the samples.

Reflectance Visible/near-infrared spectroscopy was used to analyze the effect of peel on the non-destructive soluble solids content prediction of oranges. The spectra were interpreted and partial least squares model was developed for both intact and peeled oranges at two wavelength ranges of 450–1000 and 700–1000 nm. Results showed that the wavelengths of the main absorbance peaks on the spectra of the intact and peeled samples were the same (p > 0.05). Moreover, the developed models yielded a good prediction accuracy of soluble solids content (2.5 0.05). It was also noted that the effect of peel chemical composition can be ignored in non-destructive spectroscopy of intact citrus for determination of soluble solids content.

Black bean (Phaseolus vulgaris L.) processing pre- sents unique challenges because of discoloration, breakage, development of undesirable textures, and off-flavors during canning and storage. These quality issues strongly affect processing standards and consumer acceptance for beans. In this research, visible and near-infrared (Vis/NIR) reflectance data for the spectral region of 400-2,500 nm were acquired from intact dry beans for predicting five canning quality traits, i.e., hydration coefficient (HC), visual appearance (APP) and color (COL), washed drained coefficient (WDC), and texture (TXT), using partial least squares regression (PLSR). A total of 471 bean samples harvested and canned in 2010, 2011, and 2012 were used for analysis. PLSR models based on the Vis/ NIR data showed low predictive performance, as measured by correlation coefficient for prediction (Rpred )f or APP (Rpred= 0.275-0.566) and TXT (Rpred=0.270-0.681), but better results for predicting HC (Rpred=0.517-0.810), WDC (Rpred=0.420- 0.796), and COL (Rpred

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

The freshness of rainbow trout is one of the most important quality parameters to attract customers. Common methods to detect fish freshness are usually subjective to the skill of a quality evaluator and are time consuming and destructive. Therefore, an automatic, nondestructive, accurate and quick method is needed. Hyperspectral imaging has demonstrated its efficiency in the meat and fish industries for quality control purposes. This method is nondestructive, fast and automatic. In this study, two setups for hyperspectral imaging named “Visible–Near Infrared” (Vis–NIR) and “Short Wave Infrared” (SWIR) are used to determine fish freshness. Eighty fresh rainbow trouts were divided into four batches which were separately preserved in ice for 1, 3, 5 and 7 days, respectively. Principle Component Analysis (PCA) and Partial Least Squares-Discriminate Analysis (PLS-DA) were used as unsupervised and supervised techniques for the evaluation of rainbow trout freshness. Results obtained by PCA technique indicated that four classes of samples can be detected using the Vis–NIR mean spectrum by applying a second derivative (D2) preprocessing method. The RCV2 and RPre with D2 preprocessing were 0.97 and 0.98 for Vis–NIR and 0.84 and 0.67 for SWIR, respectively. The corresponding values of RMSECV and RMSEPre were 0.16 and 0.14 in Vis–NIR and 0.44 and 0.76 in SWIR, respectively. Classification model achieved an overall correct classification of 100% and 75% for Vis–NIR and SWIR, respectively. The obtained results using both PCA and PLS-DA methods indicated that the Vis–NIR imaging system performs better than SWIR. Among all applied preprocessing techniques, the second derivative preprocessing achieved the best performance.

Visible/near infrared spectrum method has the advantages of fast and green, there is much better technical superiority in soil nutrients on-site detection. Studying on wetland soil samples from 4 districts of Qingdao with higher heterogeneity, according to its total nitrogen (TN), available nitrogen (AN), total phosphorus (TP), available phosphorus (AP), total potassium (TK), slowly available potassium (SK) and available potassium (FK) content and visible/near infrared reflectance spectroscopy, adopted SPA and PLSR to choose characteristic wavelength and build mode. Modeling results and experimental data indicated that this method to the content of TN, AN, TP, AP and FK in wetland, can be predicted accurately, it also can be predicted the content of TK and SK roughly.

The feasibility of applying rapid and non-destructive analysis based on Visible/ Near-Infrared Spectroscopy (VIS/NIR spectrometer) in detecting Malus Asiatica Nakai’s firmness during different shelf lives was discussed in this manuscript. Based on the best preprocessing technique found for each method, calibration models established by Partial Least-Square (PLS), Principal Component Regression (PCR), and Least Squares-Support Vector Machine (LS-SVM) were compared. The optimal models for firmness of Malus Asiatica Nakai were obtained by PLS with SNV as the best pretreatment. On the 1st day of shelf life, the correlation coefficients of prediction (Rp) and the Root-Mean-Square Errors of Prediction (RMSEP) were 0.7999 and 0.7994, respectively. On the 4th day of shelf life, the indexes were 0.7494 and 0.5856. But with the extension of shelf life, weightlessness rate of Malus Asiatica Nakai was reduced, the mealiness phenomenon became serious, quality had dropped sharply, hence the research of Malus Asiatica Nakai became insignificant. Results showed that, the potential of non-destructive measure for predicting accepted fruit parameters enabled the determination of optimum harvest date and fruit quality in different shelf lives.

Spectroscopy is widely recognized as an effective tool for the analysis of soil constituents. It is an efficient alternative to conventional laboratory analysis that can be cumbersome, time consuming and expensive. The majority of studies on the use of spectroscopy focus on the spectroscopic modeling to predict soil properties. However, information derived from spectra can also be used to describe the soil and how it varies across landscapes. The reason is that spectra contain information on the fundamental composition of soil: its organic matter, and iron oxide, clay and carbonate minerals as well as on water and particle size. In this study we use visible near infrared (vis–NIR) spectra to describe topsoils across Denmark. We used 693 agricultural topsoil samples (0–30cm) from the Danish soil collection and measured them with a vis–NIR spectrometer. Spectra were collected in the range between 350–2500nm. We interpreted the soils by gleaning the organic and texture information from the spectra. To summarize the information content in the spectra we performed a principal component analysis (PCA). The first three PC’s explained 94% of the variability in the spectra. The scores from the PCA were clustered using k-means to help with interpretation. Soil properties of the clusters were described using the mean spectrum of each class. We mapped the scores of the first three principal components using ordinary kriging. These maps and a cluster map derived with k-means clustering were used in the final discussion. The spectroscopic cluster map showed clearly soils with large clay contents, soils that are predominantly sandy, those that are silty and those with large amounts of organic matter, respectively. Their distribution reflects the general pattern of soil variability in Denmark. Spectroscopy is widely recognized as an effective tool for the analysis of soil constituents. It is an efficient alternative to conventional laboratory analysis that can be cumbersome, time consuming and expensive. The majority of studies on the use of spectroscopy focus on the spectroscopic modeling to predict soil properties. However, information derived from spectra can also be used to describe the soil and how it varies across landscapes. The reason is that spectra contain information on the fundamental composition of soil: its organic matter, and iron oxide, clay and carbonate minerals as well as on water and particle size. In this study we use visible near infrared (vis–NIR) spectra to describe topsoils across Denmark. We used 693 agricultural topsoil samples (0–30cm) from the Danish soil collection and measured them with a vis–NIR spectrometer. Spectra were collected in the range between 350–2500nm. We interpreted the soils by gleaning the organic and mineralogical information from the spectra. To summarize the information content in the spectra we performed a principal component analysis (PCA). The first three PC’s explained 94% of the variability in the spectra. The scores from the PCA were clustered using k-means to help with interpretation. Soil properties of the clusters were described using the mean spectrum of each class. We mapped the scores of the first three principal components using ordinary kriging. These maps and a cluster map derived with k-means clustering were used in the final discussion. The spectroscopic cluster map showed clearly soils with large clay contents, soils that are predominantly sandy, those that are silty and those with large amounts of organic matter, respectively. Their distribution reflects the general pattern of soil variability in Denmark.

Mildew growth on wheat kernels reduces grain quality due to gray discoloration of kernels that has negative impact on the resulting flour color. Higher levels of mildew damage equate to lower levels of quality. In this study, grain spectra in the 400–1,000 (visible-shortwave-near-infrared) and 1,000–2,500-nm (near-infrared) wavelength ranges were investigated for their ability to quantify mildew damage in Soft Red Winter (SRW) wheat grown in eastern Canada. For each wavelength range, partial least squares (PLS) regression models were developed for various commonly used data pre-treatments. Spectra in the 400–1,000-nm region with a mean-centering pre-treatment were optimal for quantification of mildew damage in SRW wheat. A PLS model using this approach predicted mildew damage in an independent test sample set accurately achieving a root-mean-squared-error of 0.69 and ratio-performance-deviation of 3.84 with the classification accuracy of 96 % for predictions within ±1 level against the trained inspectors’ visual assessment of mildew damage scored on a nine-level scale.

Fungal and bacterial diseases, directly infecting various parts of body, have received much attention in recent years. Bacterial infections, such as Tinea Pedis, Pityriasis versicolor and Mycetoma can secondarily occur in superficial fungal damaged skin. They often occur in immune compromised individuals including diabetics and patients with peripheral arterial diseases. Mycetoma infections can travel through the bloodstream affecting different organs. In this paper, we investigate the photo-inactivation of the pathogens causing Tinea Pedis, Pityriasis versicolor, and Mycetoma infections in three therapy resistant patients without photosensitizing drugs. We have used a combination of visible to near-infrared (VIS/NIR) laser beams in association with blue (B), red (R) and ultra-violet (UV) light emitted diodes (LEDs) with incident doses of 0.63 - 21.43 J/cm 2 . These beams have minimum side effects on the normal part of the skin. According to the physicians’ assessments, all case study patients achieved an observable progress such as decreases in inflammatory lesions, rapid process of wound healing and scars improvements. Side effects such as inflammation, crusting, or hypopigmentation were not observed. The presented irradiation protocol may be a valuable complementary treatment for patients suffering from fungal and bacterial skin infections.

Pepper is a kind of important fruit vegetable, with the expansion of pepper hybrid planting area, detection of pepper seed purity is especially important. This research used visible/near infrared (VIS/NIR) spectral technology to detect the variety of single pepper seed, and chose hybrid pepper seeds "Zhuo Jiao NO.3", "Zhuo Jiao NO.4" and "Zhuo Jiao NO.5" as research sample. VIS/NIR spectral data of 80 "Zhuo Jiao NO.3", 80 "Zhuo Jiao NO.4" and 80 "Zhuo Jiao NO.5” pepper seeds were collected, and the original spectral data was pretreated with standard normal variable (SNV) transform, first derivative (FD), and Savitzky-Golay (SG) convolution smoothing methods. Principal component analysis (PCA) method was adopted to reduce the dimension of the spectral data and extract principal components, according to the distribution of the first principal component (PC1) along with the second principal component(PC2) in the twodimensional plane, similarly, the distribution of PC1 coupled with the third principal component(PC3), and the distribution of PC2 combined with PC3, distribution areas of three varieties of pepper seeds were divided in each twodimensional plane, and the discriminant accuracy of PCA was tested through observing the distribution area of samples’ principal components in validation set. This study combined PCA and linear discriminant analysis (LDA) to identify single pepper seed varieties, results showed that with the FD preprocessing method, the discriminant accuracy of pepper seed varieties was 98% for validation set, it concludes that using VIS/NIR spectral technology is feasible for identification of single pepper seed varieties.

International audience; We report first prototypes of responsive lanthanide(III) complexes that can be monitored independently in three complementary imaging modalities. Through the appropriate choice of lanthanide(III) cations, the same reactive ligand can be used to form complexes providing detection by (i) visible (Tb3+) and near-infrared (Yb3+) luminescence, (ii) PARACEST- (Tb3+, Yb3+), or (iii) T1-weighted (Gd3+) MRI. The use of lanthanide(III) ions of different natures for these imaging modalities induces only a minor change in the structure of complexes that are therefore expected to have a single biodistribution and cytotoxicity.

Background and Aims Chemometrics combined with ultraviolet (UV), visible (VIS) and near-infrared (NIR) spectral analysis were evaluated to classify wines belonging to the controlled designation of origin (DO) Rias Baixas (Spain). Methods and Results Samples of wines made solely from the Albarino cultivar from subzones Condado, Salnes, Rosal and Ribeira do Ulla were analysed in transmittance mode using UV, VIS and NIR ranges alone or in combination. Principal component analysis was used to explore and determine data patterns. Soft independent modelling of class analogy (SIMCA), linear discriminant analysis and support vector machine classification were used to classify the samples. Condado wines were completely classified by applying SIMCA to the UV/VIS/NIR, NIR or VIS/NIR spectral ranges. Salnes and Ribeira do Ulla wines were completely classified by applying SIMCA to the UV/VIS spectral range. Finally, Rosal wines were completely classified by applying linear discriminant analysis to the VIS/NIR spectral range. Conclusions These results show that combinations of UV/VIS/NIR spectra and chemometric methods can be used as a rapid technique to classify subzones into a DO such as Rias Baixas. This technique may also assist authentication because the models also discriminated wines from outside Rias Baixas. Significance of the Study The importance of spectral range selection and chemometric tools for the classification of wines inside a DO was demonstrated.

Fusarium damage in wheat may reduce the quality and safety of food and feed products. In this study, the use of hyperspectral imaging was investigated to detect fusarium damaged kernels (FDK) in Canadian wheat samples. More than 5,200 kernels, representing seven major Canadian wheat classes, with varying degree of infection symptoms ranging from sound through mild to severe were imaged in the visible-NIR (400–1,000 nm) wavelength range. Partial least squares discriminant analysis (PLS-DA) was used to segregate kernels into sound and damaged categories based on kernel mean spectra. A universal PLS-DA model based on four wavelengths was able to detect FDK in all seven classes with an overall accuracy of 90 % and false positives of 9 %.

Radiometric calibration is a prerequisite to quantitative remote sensing, and its accuracy has a direct impact on the reliability and accuracy of the quantitative application of remote-sensing data. This article attempts to cross-calibrate the visible and near-infrared channels 1 450–520 nm, 2 520–590 nm, 3 630–690 nm, and 4 770–890 nm of the China–Brazil Earth Resources Satellite CBERS-02B/charge-coupled device CCD camera with channels 3 459–479 nm, 4 545–565 nm, 1 620–670 nm, and 2 841–876 nm of the Terra/Moderate Resolution Imaging Spectroradiometer MODIS, respectively. The radiative transfer modelling RTM method is implemented using the CBERS-02B/CCD and Terra/MODIS measurements at the Dunhuang radiometric calibration site in October 2008. The differences between the cross-calibration and vicarious calibration techniques for channels 1, 2, 3, and 4 are 11.15%, –0.89%, 2.88%, and 11.28%, respectively. Furthermore, the accuracies of the cross-calibration coefficients of CBERS-02B/CCD are analysed, wherein the results indicate that the calibration uncertainty is within 5%.

Rapid measurement of salmon flesh quality parameters (>400 samples day 1 ) was demonstrated in the laboratory and remotely at industrial sites. Visiblenear infrared spectroscopy (VNIRS) was applied to predict astaxanthin (AX) and fat content in farmed Atlantic salmon. Fish were sampled from thirteen batches (1–6 kg whole weight, containing 2.3– 16.3% fat and 1.2–12.5 l gg 1 AX), and models validated on small (average ± SD: 1.4 ± 0.4 kg) and large fish (4.2 ± 0.9 kg). Both constituents were well predicted in minced Norwegian Quality Cutlet (NQC) samples (r 2 0.86; standard error of prediction (SEP) 0.7% for fat and 0.7 l gg 1 for AX). Comparable metrics were observed for AX prediction in whole NQCs (r 2 = 0.80–0.88; SEP 0.7 l gg 1 ). Fat was better predicted in small fish than large fish for whole NQCs (r 2 = 0.82, SEP 1.0% cf r 2 = 0.59, SEP = 0.59%) and non-destructive scanning through the skin of whole, gutted fish (r 2 = 0.77, SEP = 1.2% cf r 2 = 0.49, SEP = 1.5%). Models were also developed for screening polyunsaturated fatty acid (PUFA) concentrations, e.g. in NQCs for docosahexaenoic acid (r 2 = 0.73) and n-3:n-6 PUFA (r 2 = 0.89).

Tuning the level of visible and near infrared (NIR) reflectance of textile surfaces is crucial for making them undetected in each environment. In this regard, samples of cotton/nylon fabrics were printed using a mixture of some special pigments and carbon black (CB) nanoparticles to produce brown, olive green and khaki shades which are present in concealment patterns of textiles employed in deserts. The effect of CB nanoparticles on Vis/NIR reflectance, air permeability, perspiration, light, wash fastnesses, and colorimetric values of each printed sample were evaluated. The presence of CB nanoparticles in printing formulations was found to cause significant decline in Near Infrared (NIR) reflectance of samples. The results showed that air permeability of samples printed containing CB nanoparticles are higher than samples printed with no CB particles. Absorbing phenomenon imposed by CB nanoparticles was fast against washing and perspiration, although printed samples indicated high to moderate light fastness. Furthermore, detectable change in visible appearance of the printed patterns was the main point of concern even at concentrations as low as 0.05 g/kg CB in printing formulation.

Pathogenic fungi and bacteria such as Pectobacterium atrosepticum , Clavibacter michiganensis subsp. sepedonicus , Verticillium albo-atrum , and Rhizoctonia solani were the major microorganism which causes diseases in seed potato during postharvest process. Current detection method for disease-infected seed potato relies on human inspection, which is subjective, inaccurate and labor-intensive method. In this study, a reflectance spectroscopy was used to classify sound and disease- infected seed potatoes with the spectral range from 400 to 1100 nm. Partial least square discriminant analysis (PLS-DA) with various preprocessing methods was used to investigate the feasibility of classification between sound and disease- infected seed potatoes. The classification accuracy was above 97 % for discriminating disease seed potatoes from sound ones. The results show that Vis / NIR reflectance method has good potential for non-destructive sorting for disease- infected seed potatoes. Key words

Because of the difficulties in obtaining homogenous germination of spinach seeds for baby leaf production, the possibility of using partial least squares discriminant analysis (PLS-DA) on features extracted from multispectral images of spinach seeds was investigated. The objective has been to discriminate between different seed sizes, as well as to predict germination ability and germ length. Images of 300 seeds including small, medium, and large seeds were taken, and the seeds were examined for germination ability and germ length. PLS-DA loadings plots were used to reduce the multidimensional image features to a few important features. The PLS-DA prediction resulted in an independent test set not only providing discrimination of seed size but also demonstrating how germination ability and germ length vary according to seed size. The result indicated that larger seeds had both a significantly higher germination potential and germ length compared with smaller seeds. The variable importance for projection method showed that the near infrared (NIR) wavelength region is important for germination predictability. However, the PLS-DA model did not improve when only the NIR region was used. Copyright © 2012 John Wiley & Sons, Ltd.

The objective of this study was to develop models for the predict of the milk properties (fat, protein, SNF, lactose, MUN) of unhomogenized milk using the visible and near-infrared (NIR) spectroscopic technique. A total of 180 milk samples were collected from dairy farms. To determine optimal measurement temperature, the temperatures of the milk samples were kept at three levels (, , and ). A spectrophotometer was used to measure the reflectance spectra of the milk samples. Multilinear-regression (MLR) models with stepwise method were developed for the selection of the optimal wavelength. The preprocessing methods were used to minimize the spectroscopic noise, and the partial-least-square (PLS) models were developed to prediction of the milk properties of the unhomogenized milk. The PLS results showed that there was a good correlation between the predicted and measured milk properties of the samples at and at 400~2,500 nm. The optimal-wavelength range of fat and protein were 1,600~1,800 nm, and normalization improved the prediction performance. The SNF and lactose were optimized at 1,600~1,900 nm, and the MUN at 600~800 nm. The best preprocessing method for SNF, lactose, and MUN turned out to be smoothing, MSC, and second derivative. The Correlation coefficients between the predicted and measured fat, protein, SNF, lactose, and MUN were 0.98, 0.90, 0.82, 0.75, and 0.61, respectively. The study results indicate that the models can be used to assess milk quality.

In this work is presented a method based on reflectance spectroscopy Visible-Near Infrared (VIS-NIR) to evaluate soils suspected to be contaminated by heavy metal. The concentration of Cobalt levels on several samples of soil were determinated using statistical methods (Partial-Least Square Regression) on measured data.

Palm oil content is the most important factor for quality evaluation of the fresh oil palm fruits. Evaluating the colour of fruits with the naked eye and weighing of fresh fruit bunches are employed as the rapid method for estimating the palm oil content, but may not be accurate. On the other hand, the chemical extraction method yields more reliable results but it is a time-consuming method. Therefore, a reliable and rapid method for the determination of the oil content in oil palms is required. This study investigated the feasibility of non-destructive near infrared (NIR) spectroscopy for the non-destructive determination of palm oil in oil palm fruits. A total of 42 oil palm fruit samples, at varying stages of ripeness, were examined. The samples were scanned by two NIR spectrometers with different modes; one used a diffuse reflectance mode in the long wavelength (LW) region of 1100–2500 nm and the other employed an interactance mode in the visible-short wavelength (vis-SW) region of 665–955 nm. The palm oil content was analysed by soxhlet extraction as a reference method. Partial least squares regression (PLSR) models for the determination of the palm oil content in the oil palm fruits were developed for both the vis-SW- and LW-NIR spectral regions and compared. The results showed that NIR spectroscopy can be applied for the determination of palm oil content in fresh fruits as a reliable, rapid and non-destructive method. The model developed from the LW-NIR region has modestly higher performance for correlation coefficients of determination ( R2) of 0.92, root mean square error of calibration ( RMSEC) of 1.80%w/w, root mean square error of cross-validation ( RMSECV) of 2.81%w/w, the ratio of standard deviation of reference data to RMSECV in the calibration set ( RSC) of 2.19 than those obtained from the vis-SW NIR region.