Spectroscopic analysis of chia seeds

AbstractChia seeds are becoming more and more popular in modern diets. In this contribution NIR and 2D-fluorescence spectroscopy were used to determine their nutritional values, mainly fat and protein content. 25 samples of chia seeds were analysed, whereof 9 samples were obtained from different regions in Kenya, 16 samples were purchased in stores in Germany and originated mostly from South America. For the purchased samples the nutritional information of the package was taken in addition to the values obtained for fat and protein, which were determined at the Hohenheim Core Facility. For the first time the NIR and fluorescence spectroscopy were used for the analysis of chia. For the spectral evaluation two different pre-processing methods were tested. Baseline correction with subsequent mean-centring lead to the best results for NIR spectra whereas SNV (standard normal variate transformation) was sufficient for the evaluation of fluorescence spectra. When combining NIR and fluorescence spectra, the fluorescence spectra were also multiplied with a factor to adjust the intensity levels. The best prediction results for the evaluation of the combined spectra were obtained for Kenyan samples with prediction errors below 0.2 g/100 g. For all other samples the absolute prediction error was 0.51 g/100 g for fat and 0.62 g/100 g for protein. It is possible to determine the amount of protein and fat of chia seeds by fluorescence and NIR spectroscopy. The combination of both methods is beneficial for the predictions. Chia seeds from Kenya had similar protein and lipid contents as South American seeds.

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Remote Laser Induced Fluorescence of Soils and Rocks

Photonics ◽

10.3390/photonics8100411 ◽

2021 ◽

Vol 8 (10) ◽

pp. 411

Author(s):

Vasily N. Lednev ◽

Alexey F. Bunkin ◽

Sergey M. Pershin ◽

Mikhail Ya. Grishin ◽

Diana G. Artemova ◽

...

Keyword(s):

Fluorescence Spectroscopy ◽

Fluorescence Spectra ◽

Space Exploration ◽

Laser Induced Fluorescence ◽

Excitation Wavelength ◽

Extraterrestrial Life ◽

Future Space ◽

Lidar Sensing ◽

First Time ◽

Different Soils

The laser induced fluorescence spectroscopy was systematically utilized for remote sensing of different soils and rocks for the first time, to the best of our knowledge. Laser induced fluorescence spectroscopy measurements were carried out by the developed nanosecond LIDAR instrument with variable excitation wavelength (355, 532 and 1064 nm). LIDAR sensing of different Brazil soil samples have been carried out in order to construct a spectral database. The laser induced fluorescence spectra interpretation for different samples has been discussed in detail. The perspectives of LIDAR sensing of organic samples deposited at soils and rock have been discussed including future space exploration missions in the search for extraterrestrial life.

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Identification of geographical origin of TPFD based on handheld NIR spectroscopy and PLSDA

NIR news ◽

10.1177/0960336020944007 ◽

2020 ◽

Vol 31 (5-6) ◽

pp. 25-29

Author(s):

Rita-Cindy Aye-Ayire Sedjoah ◽

Bangxing Han ◽

Hui Yan

Keyword(s):

Quality Control ◽

Near Infrared ◽

Geographical Origin ◽

Nir Spectroscopy ◽

Analysis Model ◽

First Order ◽

Standard Normal Variate ◽

Standard Normal ◽

Infrared Spectrometer ◽

Near Infrared Spectrometer

The present study is focused on the identification of geographical origin (Zhejiang, Yunnan and Anhui, China) of Dendrobium officinale’s dried stem called Tiepi fengdou by mean of the handheld near-infrared spectrometer. Raw data were preprocessed to reduce unwanted spectral variations by the first-order derivative followed by standard normal variate transformation, and partial least squares discriminant analysis model was developed for calibration. The results showed that more than 90% of the origins were identified. Therefore, it is possible to classify the geographical origin of Tiepi fengdou by the use of the handheld near-infrared spectrometer for effective quality control.

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Automatic and Rapid Discrimination of Cotton Genotypes by Near Infrared Spectroscopy and Chemometrics

Journal of Analytical Methods in Chemistry ◽

10.1155/2012/793468 ◽

2012 ◽

Vol 2012 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Hai-Feng Cui ◽

Zi-Hong Ye ◽

Lu Xu ◽

Xian-Shu Fu ◽

Cui-Wen Fan ◽

...

Keyword(s):

Discriminant Analysis ◽

Classification Accuracy ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Standard Normal Variate ◽

Pattern Recognition Methods ◽

Standard Normal ◽

Cotton Genotypes ◽

Rapid Discrimination

This paper reports the application of near infrared (NIR) spectroscopy and pattern recognition methods to rapid and automatic discrimination of the genotypes (parent, transgenic, and parent-transgenic hybrid) of cotton plants. Diffuse reflectance NIR spectra of representative cotton seeds (n=120) and leaves (n=123) were measured in the range of 4000–12000 cm−1. A practical problem when developing classification models is the degradation and even breakdown of models caused by outliers. Considering the high-dimensional nature and uncertainty of potential spectral outliers, robust principal component analysis (rPCA) was applied to each separate sample group to detect and exclude outliers. The influence of different data preprocessing methods on model prediction performance was also investigated. The results demonstrate that rPCA can effectively detect outliers and maintain the efficiency of discriminant analysis. Moreover, the classification accuracy can be significantly improved by second-order derivative and standard normal variate (SNV). The best partial least squares discriminant analysis (PLSDA) models obtained total classification accuracy of 100% and 97.6% for seeds and leaves, respectively.

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The Feasibility of Using Near-Infrared Spectroscopy and Chemometrics for Untargeted Detection of Protein Adulteration in Yogurt: Removing Unwanted Variations in Pure Yogurt

Journal of Analytical Methods in Chemistry ◽

10.1155/2013/201873 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 9

Author(s):

Lu Xu ◽

Si-Min Yan ◽

Chen-Bo Cai ◽

Zhen-Ji Wang ◽

Xiao-Ping Yu

Keyword(s):

Soy Protein ◽

Near Infrared ◽

Pure Water ◽

Nir Spectroscopy ◽

Local Market ◽

Fresh Milk ◽

Standard Normal Variate ◽

Standard Normal ◽

Modelling Techniques ◽

Different Levels

Untargeted detection of protein adulteration in Chinese yogurt was performed using near-infrared (NIR) spectroscopy and chemometrics class modelling techniques. sixty yogurt samples were prepared with pure and fresh milk from local market, and 197 adulterated yogurt samples were prepared by blending the pure yogurt objects with different levels of edible gelatin, industrial gelatin, and soy protein powder, which have been frequently used for yogurt adulteration. A recently proposed one-class partial least squares (OCPLS) model was used to model the NIR spectra of pure yogurt objects and analyze those of future objects. To improve the raw spectra, orthogonal projection (OP) of raw spectra onto the spectrum of pure water and standard normal variate (SNV) transformation were used to remove unwanted spectral variations. The best model was obtained with OP preprocessing with sensitivity of 0.900 and specificity of 0.949. Moreover, adulterations of yogurt with 1% (w/w) edible gelatin, 2% (w/w) industrial gelatin, and 2% (w/w) soy protein powder can be safely detected by the proposed method. This study demonstrates the potential of combining NIR spectroscopy and OCPLS as an untargeted detection tool for protein adulteration in yogurt.

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Feasibility of UV-Vis Spectral Fingerprinting Combined with Chemometrics for Rapid Detection of Phyllanthus niruri Adulteration with Leucaena leucocephala

Sains Malaysiana ◽

10.17576/jsm-2021-5004-10 ◽

2021 ◽

Vol 50 (4) ◽

pp. 997-1006

Author(s):

Mohamad Rafi Mohamad Rafi ◽

Bayu Nurcahyo Bayu Nurcahyo ◽

Wulan Tri Wahyuni ◽

Zulhan Arif ◽

Dewi Anggraini Septaningsih ◽

...

Keyword(s):

Leucaena Leucocephala ◽

Raw Materials ◽

Principal Component ◽

Phyllanthus Niruri ◽

Standard Normal Variate ◽

Vis Spectroscopy ◽

Standard Normal ◽

Materials Used ◽

First Time ◽

Absorbance Data

Phyllanthus niruri is widely used in Indonesia as immunostimulant. The morphology of Leucaena leucocephala leaves is similar to that of P. niruri leaves. L. leucocephala is easy to find and collect because it is widely distributed in the world. Therefore, it is likely P. niruri could be adulterated with L. leucocephala. Therefore, identification and authentication of P. niruri is important to ensure the raw materials used are original without any substitution or mixture with other similar plants causing inconsistencies in their efficacy. In this paper, we described feasibility used of UV-Vis spectral fingerprinting and chemometrics for rapid method for the identification and detection of P. niruri leaves adulterated with L. leucocephala leaves. UV-Vis spectra of samples measured in the interval of 200-800 nm and signal smoothing followed by standard normal variate were used for pre-processing the spectral data. Principal component analysis (PCA)with the absorbance data from the pre-processed UV-Vis spectra in the range of 250-700 nm as variables could distinguish P. niruri from L. leucocephala. PCA followed by discriminant analysis (DA) could successfully classified P. niruri mixed with 5, 25, and 50% L. luecocephala into their respective groups (96.81%). We also employed soft independent modelling of class analogy (SIMCA) for authentication of P. niruri and found that 88.3% of the samples were also correctly classified into their respective groups. A combination of UV-Vis spectroscopy with chemometrics, such as PCA-DA and SIMCA, were used for the first time for the identification and detection of P. niruri adulterated with L. leucocephala.

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Robust and Automated Internal Quality Grading of a Chinese Green Tea (Longjing) by Near-Infrared Spectroscopy and Chemometrics

Journal of Spectroscopy ◽

10.1155/2013/139347 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 6

Author(s):

Xian-Shu Fu ◽

Lu Xu ◽

Xiao-Ping Yu ◽

Zi-Hong Ye ◽

Hai-Feng Cui

Keyword(s):

Quality Control ◽

Green Tea ◽

Near Infrared ◽

Nir Spectroscopy ◽

Internal Quality Control ◽

Internal Quality ◽

Outlier Analysis ◽

Standard Normal Variate ◽

Standard Normal ◽

Quality Grading

Near-infrared (NIR) spectroscopy and chemometric methods were applied to internal quality control of a Chinese green tea, Longjing, with Protected Geographical Indication (PGI). A total of 2745 authentic Longjing tea samples of three different grades were analyzed by NIR spectroscopy. To remove the influence of abnormal samples, The Stahel-Donoho estimate (SDE) of outlyingness was used for outlier analysis. Partial least squares discriminant analysis (PLSDA) was then used to classify the grades of tea based on NIR spectra. Different data preprocessing methods, including smoothing, taking second-order derivative (D2) spectra, and standard normal variate (SNV) transformation, were performed to reduce unwanted spectral variations in samples of the same grade before classification models were developed. The results demonstrate that smoothing, taking D2 spectra, and SNV can improve the performance of PLSDA models. With SNV spectra, the model sensitivity was 1.000, 0.955, and 0.924, and the model specificity was 0.979, 0.952, and 0.996 for samples of three grades, respectively. FT-NIR spectrometry and chemometrics can provide a robust and effective tool for rapid internal quality control of Longjing green tea.

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A Chemometrics Approach for Distinguishing between Beers Using near Infrared Spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.830 ◽

2009 ◽

Vol 17 (2) ◽

pp. 69-76 ◽

Cited By ~ 11

Author(s):

Hua Li ◽

Yutaka Takahashi ◽

Masanori Kumagai ◽

Kazuhiko Fujiwara ◽

Ryoei Kikuchi ◽

...

Keyword(s):

Quality Evaluation ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Total Polyphenol ◽

Standard Normal Variate ◽

Production Processes ◽

Standard Normal ◽

Akita Prefecture ◽

Beer Production

Thirty-eight beers from different producing areas and/or makers were distinguished by principal component analysis (PCA) of the near infrared (NIR) spectra acquired by a portable NIR spectrometer. Classsification of Akita beers: beers locally produced in Akita prefecture, Japan, from other famous brand beers could be successfully performed, especially when the PCA was calculated on the standard normal variate (SNV) spectra. The classification equations use information related to water and CH2 absorption that reflected the differences in chemical com position of beers due to different production processes. In addition, the compositions of total polyphenol and total nitrogen were estimated from NIR spectra by multiple linear regression (MLR). This study showed that NIR spectroscopy is promising for beer quality evaluation, both for identifying multifarious beers including Akita beers using PCA and for rapid in-line quality control and inspection for beer production using the quantitative MLR analysis.

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Application of calibrations to hyperspectral images of food grains: example for wheat falling number

Journal of Spectral Imaging ◽

10.1255/jsi.2017.a4 ◽

2017 ◽

Cited By ~ 7

Author(s):

Nicola Caporaso ◽

Martin Whitworth ◽

Ian Fisk

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Classification Model ◽

Least Squares Regression ◽

Falling Number ◽

Linear Discriminant ◽

Standard Normal Variate ◽

Average Spectrum ◽

Standard Normal ◽

Improved Performance

The presence of a few kernels with sprouting problems in a batch of wheat can result in enzymatic activity sufficient to compromise flour functionality and bread quality. This is commonly assessed using the Hagberg Falling Number (HFN) method, which is a batch analysis. Hyperspectral imaging (HSI) can provide analysis at the single grain level with potential for improved performance. The present paper deals with the development and application of calibrations obtained using an HSI system working in the near infrared (NIR) region (~900–2500 nm) and reference measurements of HFN. A partial least squares regression calibration has been built using 425 wheat samples with a HFN range of 62–318 s, including field and laboratory pre-germinated samples placed under wet conditions. Two different approaches were tested to apply calibrations: i) application of the calibration to each pixel, followed by calculation of the average of the resulting values for each object (kernel); ii) calculation of the average spectrum for each object, followed by application of the calibration to the mean spectrum. The calibration performance achieved for HFN (R2 = 0.6; RMSEC ~ 50 sRMSEP ~ 63 s) compares favourably with other studies using NIR spectroscopy. Linear spectral pre-treatments lead to similar results when applying the two methods, while non-linear treatments such as standard normal variate showed obvious differences between these approaches. A classification model based on linear discriminant analysis (LDA) was also applied to segregate wheat kernels into low (<250 s) and high (>250 s) HFN groups. LDA correctly classified 86.4% of the samples, with a classification accuracy of 97.9% when using an HFN threshold of 150 s. These results are promising in terms of wheat quality assessment using a rapid and non-destructive technique which is able to analyse wheat properties on a single-kernel basis, and to classify samples as acceptable or unacceptable for flour production.

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Classification of Floral Origins of Honey by NIR and Chemometrics

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.605-607.905 ◽

2012 ◽

Vol 605-607 ◽

pp. 905-909 ◽

Cited By ~ 2

Author(s):

Xiu Ying Liang ◽

Xiao Yu Li ◽

Wen Jun Wu

Keyword(s):

Spectral Data ◽

Spectral Region ◽

Near Infrared ◽

Back Propagation ◽

Nir Spectroscopy ◽

Classification Rate ◽

Standard Normal Variate ◽

Mean Centering ◽

Scattering Correction ◽

Standard Normal

Near-infrared (NIR) spectroscopy combined with chemometrics methods has been investigated to discriminate type of honey. 147 NIR spectra of six floral origins of honey samples were collected within 4000~10000cm-1 spectral region. Spectral data were compressed using partial least squares (PLS). Back propagation neural networks (BPNN) models were constructed to distinguish the type of honey. Six spectral data pretreatments including first derivative, first derivatives followed by mean centering(MC), second derivatives, Savitzky-Golay smoothing, standard normal variate transformation (SNV) and multiplicative scattering correction (MSC) were compared to establish the optimal models for honey discrimination. Savitzky-Golay smoothing proved more effective than the other data pretreatments. BPNN models were developed within the full spectral region, 5303～6591cm-1 and 7012～10001cm-1, respectively. Results have shown that the highest(100%) classification rate was achieved within 5303～6591cm-1 wave range. Our results indicated that NIR spectroscopy with chemometrics techniques can be applied to classify rapidly honeys of different floral origin.

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Development of a Non-Destructive Method for Detection of the Juiciness of Pear via VIS/NIR Spectroscopy Combined with Chemometric Methods

Foods ◽

10.3390/foods9121778 ◽

2020 ◽

Vol 9 (12) ◽

pp. 1778

Author(s):

Fan Wang ◽

Chunjiang Zhao ◽

Guijun Yang

Keyword(s):

Near Infrared ◽

External Validation ◽

Scatter Correction ◽

Nir Spectroscopy ◽

Spectral Ratio ◽

Optical Path Length ◽

Standard Normal Variate ◽

Length Estimation ◽

Standard Normal ◽

Non Destructive

Juiciness is a primary index of pear quality and freshness, which is also considered as important as sweetness for the consumers. Development of a non-destructive detection method for pear juiciness is meaningful for producers and sellers. In this study, visible−near-infrared (VIS/NIR) spectroscopy combined with different spectral preprocessing methods, including normalization (NOR), first derivative (FD), detrend (DET), standard normal variate (SNV), multiplicative scatter correction (MSC), probabilistic quotient normalization (PQN), modified optical path length estimation and correction (OPLECm), linear regression correction combined with spectral ratio (LRC-SR) and orthogonal spatial projection combined with spectral ratio (OPS-SR), was used for comparison in detection of pear juiciness. Partial least squares (PLS) regression was used to establish the calibration models between the preprocessing spectra (650–1100 nm) and juiciness measured by the texture analyzer. In addition, competitive adaptive reweighted sampling (CARS) was used to identify the characteristic wavelengths and simplify the PLS models. All obtained models were evaluated via Monte Carlo cross-validation (MCCV) and external validation. The PLS model established by 19 characteristic variables after LRC-SR preprocessing displayed the best prediction performance with external verification determination coefficient (R2v) of 0.93 and root mean square error (RMSEv) of 0.97%. The results demonstrate that VIS/NIR coupled with LRC-SR method can be a suitable strategy for the quick assessment of juiciness for pears.

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