A method for highlighting differences between bacteria grown on nutrient agar using near infrared spectroscopy and principal component analysis

Fast diagnostic tools such as near infrared spectroscopy have recently gained interest for bacterial identification. To avoid a process involving microbial pellet or suspension preparation from Petri dishes for NIR analysis, direct screening from agar in Petri dishes was explored. This two-step study proposes a new procedure for bacterial screening directly on agar plates with minimal nutrient medium bias. Firstly, principal component analyses showed optimal discrimination between the genera Lactobacillus, Pseudomonas and Brochothrix on different culture media, in transmission mode and with the bottom of Petri dishes facing the light source. The repeatability of spectra in these conditions was assessed with an average coefficient of variation inferior to 5% in the 12,500–3680 cm−1 range. Secondly, 40 strains of Lactococcus and Enterococcus species were grown on Bennett agar and measured over a series of five assays. Principal component analyses highlighted better clustering according to genera and species and lower external bias while retaining the 8790–3680 cm−1 spectral range and applying an extended multiplicative scatter correction with an average agar spectrum as a reference, in comparison to raw data and standard multiplicative scatter correction.

Remediation-related monitoring of heavy metal concentration of contaminated Technosol using hyperspectral measurements

<p>Across Europe there are 2.5 million potentially contaminated sites, approximately one third have already been identified and around 15% have been sanitized. Phytoremediation is a well-established technique to tackle this problem and to rehabilitate soil. However, remediation methods, such as biological treatments with microorganisms or phytoremediation with trees, are still relatively time consuming. A fast monitoring of changes in heavy metal content over time in contaminated soils with hyperspectral spectroscopy is one of the first key factors to improve and control existing bioremediation methods.</p><p>At former sewage farms near Ragow (Brandenburg, Germany), 110 soil samples with different contamination levels were taken at a depth between 15-20 cm. These samples were prepared for hyperspectral measurements using the HySpex system under laboratory conditions, combing a VNIR (400-1000 nm) and a SWIR (1000-2500 nm) line-scan detector. Different spectral pre-processing methods, including continuum removal, first and second derivatives, standard normal variate, normalisation and multiplicative scatter correction, with two established estimation models such as Partial Least Squares Regression (PLSR) and Random Forest Regression (RFR), were applied to predict the heavy metal concentration (Ba, Ni, Cr, Cu) of this specific Technosol. The coefficient of determination (R2) shows for Ba and Ni values between 0.50 (RMSE: 9%) and 0.61 (RMSE: 6%) for the PLSR and between 0.84 (RMSE: 0.03%) and 0.91 (RMSE: 0.02%) for the RFR model. The results for Cu and Cr show values between 0.57 (RMSE: 17.9%) and 0.69 (RMSE: 15%) for the PLSR and 0.86 (0.12%) and 0.93 (0.01%) for the RFR model. The pre-processing method, which improve the robustness and performance of both models best, is multiplicative scatter correction followed by the standard normal variate for the first and second derivatives. Random Forest in a first approach seems to deliver better modeling performances. Still, the pronounced differences between PLSR and RFR fits indicate a strong dependence of the results on the respective modelling technique. This effect is subject to further investigation and will be addressed in the upcoming analysis steps.</p>

Investigating aquaphotomics for temperature-independent prediction of soluble solids content of pure apple juice

The methods of aquaphotomics were explored as an aid to improve near infrared spectroscopic predictive modelling of the soluble solids content of pure apple juice at different temperatures. The study focussed on the first overtone region of the O–H stretching vibration of water (1300–1600 nm). A transmission-based FT-NIR (Fourier transform near infrared) spectrometer was used to acquire 103 spectra of freshly expressed juice samples from individual ‘Braeburn’ apples over the wavelength range of 870–1800 nm with a 1 mm cuvette at three temperatures, 20, 25 and 30°C. The aquagram of the first overtone water region showed a trend of increasing bound water absorption with rising soluble solids content, from 7.3 to 13.7°Brix, and increasing free water absorption with rising temperature from 20 to 30°C. Predictive models for apple juice soluble solids content at 25°C were developed using partial least squares regression with spectral pre-processing by standard normal variate (SNV) followed by second derivative transformation (SNV + 2D) or no pre-processing on absorbance spectra at all. The best result, with lowest standard error of prediction of 0.38°Brix, was obtained using the first overtone water region with partial least squares regression on the SNV + 2D spectra. The method of extended multiplicative scatter correction was used, as an additional pre-processing step, to improve apple juice soluble solids content prediction at different temperatures. The interference component selected for the extended multiplicative scatter correction method was the first principal component loading measured using pure water samples taken at the same three temperatures (20, 25 and 30°C). Such extended multiplicative scatter correction pre-processing greatly reduced the soluble solids content prediction bias, when applying the partial least squares regression model developed at 20°C to samples measured at 25 and 30°C, from 0.23 to 0.08 and 0.36 to 0.13°Brix, respectively. Model precision (in terms of standard error of prediction) was also slightly improved by 0.02°Brix in each case, from 0.40 to 0.38 and 0.46 to 0.44°Brix at 25 and 30°C respectively.

Penggunaan Teknologi UV-Vis Spectroscopy untuk Membedakan Jenis Kopi Bubuk Arabika Gayo Wine dan Kopi Bubuk Arabika Gayo Biasa

Salah satu kopi spesialti Indonesia adalah kopi Arabika Gayo wine yang merupakan varietas hasil seleksi yang dikembangkan oleh petani Indonesia. Penelitian ini merupakan penelitian kualitatif yang bertujuan untuk membangun dan menguji model diskriminasi untuk mengidentifikasi dan mengklasifikasikan kopi bubuk Arabika Gayo wine dan kopi bubuk Arabika Gayo biasa. Komposisi bahan yang digunakan dalam penelitian ini yaitu 1 gram dengan jumlah 100 sampel kopi bubuk Gayo wine, dan 100 sampel kopi bubuk Gayo biasa. Pengujian dilakukan pada bubuk kopi berukuran 0,297 milimeter (mesh 50). Setiap sampel diekstraksi menggunakan air aquades dengan suhu 90-98ºC, kemudian disaring dan diencerkan menggunakan aquades dengan perbandingan 1:20. Pengambilan spektra pada sampel hasil ekstraksi dilakukan sebanyak 2 kali pengulangan untuk setiap sampel menggunakan Spektrometer UV-Vis Genesis 10s pada rentang panjang gelombang 190-1100 nm. Data spektra diolah menggunakan metode PCA untuk melihat pengelompokkan semua data. Setelah itu, untuk model diskriminasi dibangun menggunakan metode SIMCA untuk spektra original dan petreatment. Hasil klasifikasi terbaik yaitu pada metode multiplicative scatter correction (MSC) dan moving average 9s yang menjelaskan nilai keragaman data dengan nilai PC1 97% dan PC2 3%. Sedangkan untuk klasifikasi data diperoleh nilai akurasi (AC) 100%, spesifisitas (SP) 100%, dan sensitivitas (S) 100%, dengan nilai eror (FP) 0%. Berdasarkan hasil ini pada semua pengujian. maka model SIMCA yang dibangun dapat mengidentifikasi dan mengklasifikasikan sampel kopi prediksi ke dalam kelas yang sesuai dengan baik. Kata Kunci: Kopi Arabika Gayo biasa, kopi Arabika Gayo wine, UV-Vis spectroscopy, PCA, SIMCA.

A study on prediction performance of the mechanical properties of rubber using Fourier-transform near infrared spectroscopy

Near infrared spectroscopy is a spectroscopic method used for quality and quantity analysis of agriculture products and industry materials. Rubber is a mostly raw material of any products. NIR spectroscopy had been using to analyze the mechanical properties of rubber and polymer materials. Prediction models were built from the correlation between the NIR spectra and mechanical strength values (hardness and tensile strength). Raw data were pretreated to improve the prediction models, where the prediction models were based on partial least squares regression and support vector regression. In the case of hardness prediction, the raw dataset was pretreated with standard normal variate transformation or a combination of Savitzky–Golay smoothing and multiplicative scatter correction, following which orthogonal signal correction and uninformative variable elimination were used for feature selection, and partial least squares regression and support vector regression were applied for the prediction model. For tensile strength prediction, the pretreatments were multiplicative scatter correction or combination of Savitzky–Golay smoothing and multiplicative scatter correction, following which orthogonal signal correction and uninformative variable elimination were used for feature selection, and partial least squares regression and support vector regression were applied for the prediction model. From these processes, the r2 values were greater than 0.9, the bias values were among ±0.5, and the RMSEP values were lower than 5.

Accuracy and stability improvement in detecting Wuchang rice adulteration by piece-wise multiplicative scatter correction in the hyperspectral imaging system

The adulterated Wuchang rice were detected using hyperspectral imaging system with the optimal spectral pre-processing methods.