Rapid Nondestructive Detection Research Articles

Variation Analysis of Starch Properties in Tartary Buckwheat and Construction of Near-Infrared Models for Rapid Non-Destructive Detection

Due to the requirements for quality testing and breeding Tartary buckwheat (Fagopyrum tartaricum Gaerth), it is necessary to find a method for the rapid detection of starch content in Tartary buckwheat. To obtain samples with a continuously distributed chemical value, stable Tartary buckwheat recombinant inbred lines were used. After scanning the near-infrared spectra of whole grains, we employed conventional methods to analyze the contents of Tartary buckwheat. The results showed that the contents of total starch, amylose, amylopectin, and resistant starch were 532.1–741.5 mg/g, 176.8–280.2 mg/g, 318.8–497.0 mg/g, and 45.1–105.2 mg/g, respectively. The prediction model for the different starch contents in Tartary buckwheat was established using near-infrared spectroscopy (NIRS) in combination with chemometrics. The Kennard–Stone algorithm was used to split the training set and the test set. Six different methods were used to preprocess the spectra in the wavenumber range of 4000–12,000 cm−1. The Competitive Adaptive Reweighted Sampling algorithm was then used to extract the characteristic spectra, and the prediction model was built using the partial least squares method. Through a comprehensive analysis of each parameter of the model, the best model for the prediction of each nutrient was determined. The correlation coefficient of calibration (Rc) and the correlation coefficient of prediction (Rp) of the best models for total starch and amylose were greater than 0.95, and the Rc and Rp of the best models for amylopectin and resistant starch were also greater than 0.93. The results showed that the NIRS-based prediction model fulfilled the requirement for the rapid determination of Tartary buckwheat starch, thus providing an effective technical approach for the rapid and non-destructive testing of starch content in the food science and agricultural industry.

Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology.

(1) Background: To achieve the rapid, non-destructive detection of corn freshness and staleness for better use in the storage, processing and utilization of corn. (2) Methods: In this study, three varieties of corn were subjected to accelerated aging treatment to study the trend in fatty acid values of corn. The study focused on the use of hyperspectral imaging technology to collect information from corn samples with different aging levels. Spectral data were preprocessed by a convolutional smoothing derivative method (SG, SG1, SG2), derivative method (D1, D2), multiple scattering correction (MSC), and standard normal transform (SNV); the characteristic wavelengths were extracted by the competitive adaptive reweighting method (CARS) and successive projection algorithm (SPA); a neural network (BP) and random forest (RF) were utilized to establish a prediction model for the quantification of fatty acid values of corn. And, the distribution of fatty acid values was visualized based on fatty acid values under the corresponding optimal prediction model. (3) Results: With the prolongation of the aging time, all three varieties of corn showed an overall increasing trend. The fatty acid value of corn can be used as the most important index for characterizing the degree of aging of corn. SG2-SPA-RF was the quantitative prediction model for optimal fatty acid values of corn. The model extracted 31 wavelengths, only 12.11% of the total number of wavelengths, where the coefficient of determination RP2 of the test set was 0.9655 and the root mean square error (RMSE) was 3.6255. (4) Conclusions: This study can provide a reliable and effective new method for the rapid non-destructive testing of corn freshness.

Effect of the void and crack quantity and location on ultrasound: resin experiments and carbon brick validation

ABSTRACT In industrial applications, there is a lack of methods applicable to large-scale, rapid and non-destructive detection of voids and cracks inside carbon bricks. This paper used the ultrasonic testing (UT) A-scan method to investigate the influence of quantity (void fraction:1.4%-4.6%; number of artificial cracks:1–7) and location (five layers) of voids and cracks inside the resin on ultrasonic waves. Both fast Fourier transform (FFT) and synchrosqueezed wavelet transform (SWT) are employed to analyse the amplitude, main frequency and normalised peak time of ultrasonic waves. The results indicate that increased void fraction and number of cracks lead to greater amplitude attenuation, promoting the transition from the fundamental wave to harmonics. Void and crack location symmetrically affect ultrasound amplitude, main frequency, and normalised peak time around the sample centre. Meanwhile, fitting equations are established for the main frequency of the fundamental wave (f 1) in relation to defect quantity. A high-precision quantitative defects detection standard is also developed based on normalised peak time. The validation based on carbon bricks indicates that the derived empirical relations between these parameters and defects can lay the foundation for large-scale and rapid non-destructive detection in carbon bricks.

Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review.

Traditionally, tenderness has been assessed through shear force testing, which is inherently destructive, the accuracy is easily affected, and it results in considerable sample wastage. Although this technology has some drawbacks, it is still the most effective detection method currently available. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into applying five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of the quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations to guide the future direction of non-destructive, AI-enhanced meat tenderness detection.

Recyclable Fe3O4@SiO2@PEI-DTC@Au-Ag nanocomposites as a SERS platform for sensitive detection of thiram on fruit surfaces

Equipped with surface-enhanced Raman scattering (SERS) effect and magnetic manipulation capacity, a new paradigm of recyclable SERS substrates based on Fe3O4 nanoparticles (NPs) @SiO2@PEI-DTC adhesive layer @Au-Ag alloy quantum dots (QDs) were successfully prepared via engineering hydrothermal and seed deposition techniques. The SERS performance and uniformity of those substrates with different gold-silver (Au-Ag) weight ratios were evaluated by 4-aminothiophenol (4-ATP) molecules, revealing that the relative standard deviations (RSDs) of all substrates were less than 12 %, with the optimal SERS substrate achieving an enhancement factor (EF) of up to 1.39 × 105. This phenomenon can be attributed to the magnetic core and plasmonic resonance properties of Au-Ag alloy QDs, which result in abundant interparticle hotspots in the Fe3O4@SiO2@PEI-DTC@Au-Ag (FSPAA) nanocomposites. Furthermore, the optimal FSPAA nanocomposite was employed as a SERS substrate for rapid non-destructive detection of thiram on fruit surfaces. The SERS signal intensity exhibited a robust manifesting a linear association with thiram concentration within the span of 1 × 10−4 to 1 × 10−9 M, and the minimal detectable concentration was 1.04 × 10−10 M. The FSPAA core–shell nanocomposites exhibit high reusability and reliability and hold great promise for the practical applications in on-site assessment of food/environmental safety, as well as in spectroscopic identification of molecules adsorbed onto fruit surfaces.

Rapid and non-destructive quality estimation of cinchona, andrographis paniculata, and black pepper using a portable NIR spectroscopy measuring device

Quality assessment of natural products are mostly done by laboratory methods which are invasive, cost-intensive, with low-throughput and prone to human error. Near infrared (NIR) spectroscopy have emerged as a very useful technique as a rapid non-destructive detection and quality estimation tool in identifying chemical fingerprint sensitive to the vibrational modes in chemical bonds like O-H, C-H and N-H.In this work, a reasonably low-cost and portable NIR spectrometer was developed with a few imported internal components, together with compatible software for its use, like the graphical user interface (GUI) and calibration program. Quinine in Cinchona (Cinchona L) bark, andrographolides in Andrographis paniculata and piperine in black pepper were estimated and the coefficient of determination (R2) of 0.93, 0.94, and 0.90 was obtained respectively for the regression analysis. The model for cinchona, andrographolides, and piperine gave residual prediction deviation (RPDs) of 4.25, 4.21 and 3.0, range error ratio (RERs) of 14.59, 14.42, 10.32 and the ratio between the standard error of calibration (SEC) and the standard error of prediction (SEP) i.e. SEP/SEC of 1.18, 0.21 and 0.92 respectively. The derived partial least squares (PLS) regression models fulfil the requirements of AACC Method 39–00 (AACC in AACC Method, 39–00:15, 1999) used for screening (RPD ≥ 2.5), as per knowledge of the authors. The device can be used for other molecular markers in plants and their byproducts.

Research Progress of Rapid Non-Destructive Detection Technology in the Field of Apple Mold Heart Disease.

Apples are rich in vitamins and dietary fiber and are one of the essential fruits in people's daily diet. China has always been a big apple consumer, and with the improvement of people's life quality, nutrition, and health requirements, the demand for high-quality apples has increased year by year. Apple mold heart disease is one of the main diseases affecting apple quality. However, this disease cannot be easily detected from the surface, so it is difficult to detect mold heart disease. Therefore, this paper focuses on the analysis of seven non-destructive detection technologies, including near infrared spectroscopy technology, hyperspectral technology, Raman spectroscopy technology, electronic nose technology, acoustic technology, electrical technology, and magnetic technology, summarizes their application status in the detection of apple mold heart disease, and then analyzes their advantages and disadvantages. Combined with the current rapid development of artificial intelligence (AI) technology, this paper proposes the future development trends of using non-destructive technologies to detect apple mold heart disease. It is expected to provide basic theory and application references for the intelligent detection of apple mold heart disease.

The Rapid Non-Destructive Detection of the Protein and Fat Contents of Sorghum Based on Hyperspectral Imaging

The Rapid Non-Destructive Detection of the Protein and Fat Contents of Sorghum Based on Hyperspectral Imaging

Combining Vis-NIR and NIR Spectral Imaging Techniques with Data Fusion for Rapid and Nondestructive Multi-Quality Detection of Cherry Tomatoes.

Firmness, soluble solid content (SSC) and titratable acidity (TA) are characteristic substances for evaluating the quality of cherry tomatoes. In this paper, a hyper spectral imaging (HSI) system using visible/near-infrared (Vis-NIR) and near-infrared (NIR) was proposed to detect the key qualities of cherry tomatoes. The effects of individual spectral information and fused spectral information in the detection of different qualities were compared for firmness, SSC and TA of cherry tomatoes. Data layer fusion combined with multiple machine learning methods including principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR) and back propagation neural network (BP) is used for model training. The results show that for firmness, SSC and TA, the determination coefficient R2 of the multi-quality prediction model established by Vis-NIR spectra is higher than that of NIR spectra. The R2 of the best model obtained by SSC and TA fusion band is greater than 0.9, and that of the best model obtained by the firmness fusion band is greater than 0.85. It is better to use the spectral bands after information fusion for nondestructive quality detection of cherry tomatoes. This study shows that hyperspectral imaging technology can be used for the nondestructive detection of multiple qualities of cherry tomatoes, and the method based on the fusion of two spectra has a better prediction effect for the rapid detection of multiple qualities of cherry tomatoes compared with a single spectrum. This study can provide certain technical support for the rapid nondestructive detection of multiple qualities in other melons and fruits.

Conformal Temperature/Impedance Sensing Patch Based on Graphene Materials for Nondestructive Detection of Fish Freshness.

Rapid nondestructive detection of fish freshness is essential to ensure food safety and nutrition. In this study, we demonstrate a conformal temperature/impedance sensing patch for temperature monitoring, as well as freshness classification during fish storage. The optimization of the flexible laser-induced graphene electrodes is studied based on both simulation and experimental validation, and dimensional accuracy of 5‰ and high impedance reproducibility are obtained. A laser-assisted thermal reduction technology is innovatively introduced to directly form a reduced graphene oxide-based temperature-sensitive layer on the surface of a flexible substrate. The comprehensive performance is superior to that of most reported temperature-sensitive devices based on graphene materials. As an application demonstration, the fabricated flexible dual-parameter sensing patch is conformed to the surface of a refrigerated fish. The patch demonstrates the ability to accurately sense low temperatures in a continuous 120 min monitoring, accompanied by no interference from high humidity. Meanwhile, the collected impedance data are imported into the support vector machine model to obtain a freshness classification accuracy of 93.07%. The conformal patch integrated with crosstalk-free dual functions costs less than $1 and supports free customization, providing a feasible methodology for rapid nondestructive detection or monitoring of food quality.

Maize seed appearance quality assessment based on improved Inception-ResNet.

Current inspections of seed appearance quality are mainly performed manually, which is time-consuming, tedious, and subjective, and creates difficulties in meeting the needs of practical applications. For rapid and accurate identification of seeds based on appearance quality, this study proposed a seed-quality evaluation method that used an improved Inception-ResNet network with corn seeds of different qualities. First, images of multiple corn seeds were segmented to build a single seed image database. Second, the standard convolution of the Inception-ResNet module was replaced by a depthwise separable convolution to reduce the number of model parameters and computational complexity of the network. In addition, an attention mechanism was applied to improve the feature learning performance of the network model and extract the best image information to express the appearance quality. Finally, the feature fusion strategy was used to fuse the feature information at different levels to prevent the loss of important information. The results showed that the proposed method had decent comprehensive performance in detection of corn seed appearance quality, with an average of 96.03% for detection accuracy, 96.27% for precision, 96.03% for recall rate, 96.15% for F1 value of reconciliation, and the average detection time of an image was about 2.44 seconds. This study realized rapid nondestructive detection of seeds and provided a theoretical basis and technical support for construction of intelligent seed sorting equipment.

A Sustainable Way to Determine the Water Content in Torreya grandis Kernels Based on Near-Infrared Spectroscopy

Water content is an important parameter of Torreya grandis (T. grandis) kernels that affects their quality, processing and storage. The traditional drying method for water content determination is time-consuming and laborious. Water content detection based on modern analytical techniques such as spectroscopy is accomplished in a fast, accurate, nondestructive, and sustainable way. The aim of this study was to realize the rapid detection of the water content in T. grandis kernels using near-infrared spectroscopy. The water content of T. grandis kernels was measured by the traditional drying method. Meanwhile, the corresponding near-infrared spectra of these samples were collected. A quantitative water content model of T. grandis kernels was established using the full spectrum after 10 outlier samples were removed by the Mahalanobis distance method and concentration residual analysis. The results showed that the prediction model developed from the partial least squares regression (PLS) method after the spectra were pretreated by the standard normal variate transform (SNV) achieved optimal performance. The correlation coefficient of the calibration set (R2c) and the cross-validation set (R2cv) were 0.9879 and 0.9782, respectively, and the root mean square error of the calibration set (RMSEC) and the root mean square error of the cross-validation set (RMSECV) were 0.0029 and 0.0039, respectively. Thus, near-infrared spectroscopy is feasible for the rapid nondestructive detection of the water content in T. grandis seeds. Detecting the water content of agricultural and forestry products in such an environmentally friendly manner is conducive to the sustainable development of agriculture.

Establishment and Accuracy Evaluation of Cotton Leaf Chlorophyll Content Prediction Model Combined with Hyperspectral Image and Feature Variable Selection

In order to explore the feasibility of rapid non-destructive detection of cotton leaf chlorophyll content during the growth stage, this study utilized hyperspectral technology combined with a feature variable selection method to conduct quantitative detection research. Through correlation spectroscopy (COS), a total of 882 representative samples from the seedling stage, bud stage, and flowering and boll stage were used for feature wavelength screening, resulting in 213 selected feature wavelengths. Based on all wavelengths and selected feature wavelengths, a backpropagation neural network (BPNN), a backpropagation neural network optimized by genetic algorithm (GA-BPNN), a backpropagation neural network optimized by particle swarm optimization (PSO-BPNN), and a backpropagation neural network optimized by sparrow search algorithm (SSA-BPNN) prediction models were established for cotton leaf chlorophyll content, and model performance comparisons were conducted. The research results indicate that the GA-BPNN, PSO-BPNN, and SSA-BPNN models established based on all wavelengths and selected feature wavelengths outperform the BPNN model in terms of performance. Among them, the SSA-BPNN model (referred to as COS-SSA-BPNN model) established using 213 feature wavelengths extracted through correlation analysis showed the best performance. Its determination coefficient and root-mean-square error for the prediction set were 0.920 and 3.26% respectively, with a relative analysis error of 3.524. In addition, the innovative introduction of orthogonal experiments validated the performance of the model, and the results indicated that the optimal solution for achieving the best model performance was the SSA-BPNN model built with 213 feature wavelengths extracted using the COS method. These findings indicate that the combination of hyperspectral data with the COS-SSA-BPNN model can effectively achieve quantitative detection of cotton leaf chlorophyll content. The results of this study provide technical support and reference for the development of low-cost cotton leaf chlorophyll content detection systems.

The rapid detection of the tannin content of grains based on hyperspectral imaging technology and chemometrics

Tannins are one of the key components that constitute the nutritional structure of grains, and have a significant impact on the quality of grain products. The detection of tannins by chemical methods is both time- and manpower-consuming. In this study, hyperspectral imaging (HSI) technology was combined with an optimized algorithm, iteratively variable subset optimization (IVSO), in conjunction with the variable importance in projection (VIP) method to rapidly detect the tannin content of grains (sorghum, rice, glutinous rice, wheat, and corn). The IVSO algorithm and VIP method were employed to extract characteristic wavelengths. Subsequently, three tannin content prediction models based on full and characteristic wavelengths (BPNN, PSO-LSSVM, and DF) were respectively established. A comparative analysis demonstrated that the DF model based on the characteristic wavelengths had the best effect (Rp2 = 0.9922 g/100 g, RPD = 11.3), and the tannin content of grain particles was visually analyzed by using the best prediction model. The results show that HSI can realize the rapid non-destructive detection of the tannin content of grain particles, which provides a new technical reference for the food industry to improve product quality.

Research on nondestructive detection of pine nut quality based on terahertz imaging

ObjectivePine nuts are of great nutritional and medicinal value, but they cannot avoid such defects as mildew and insect-eating during storage. Because of their hard shells, the internal quality detection of pine nuts is a major problem for industrial sorting. For this reason, it is of great significance to carry out rapid nondestructive detection of the internal quality of pine nuts. MethodsIn this paper, a rapid and nondestructive detection of pine nuts for mildew and plumpness based on terahertz transmission imaging technology was carried out. Firstly, the terahertz transmission images of pine nut samples were acquired and the terahertz spectral signals of four different regions of interest were extracted for analysis. Secondly, in order to reduce the interference of external environment on the acquisition of terahertz spectra, the terahertz spectra were pre-processedby several methods, such as ALS, AirPLS, BEADS and SNV + Detrending, and then three qualitative discriminant models, namely, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and XGBoost integration learning, were established respectively to explore the optimal qualitative discriminant model for the detection of pine nut quality. Finally, the terahertz transmission image of pine nuts was subjected to image processing. The main feature gamut extraction combined with channel separation strategy were adopted. and then the automatic threshold segmentation algorithm was applied to perform binary threshold segmentation on the separated image, thus the plumpness of the pine nuts was calculated by calculating the ratio of the pixel points of the shell and the kernel. ResultsThe prediction set of BEADS + XGBoost model was established after data preprocessing with the optimal effect and the accuracy of 98.61%. The acquired terahertz images of pine nuts were extracted by the main feature gamut and the images of channel B were extracted by using channel separation. Finally, the automatic threshold segmentation of channel B was performed by using the maximum one-dimensional entropy, which can well realize the visual detection of the inner shell kernel of pine nuts. ConclusionTerahertz imaging technology can achieve rapid and nondestructive detection of pine mildew as well as pine nut plumpness. This study provides a new rapid and nondestructive effective method for pine nut quality detection, which can provide technical reference for other shelled nut quality detection and has significant practical value.

A Rapid Non-destructive Detection Method for Wolfberry Moisture Grade Using Hyperspectral Imaging Technology

A Rapid Non-destructive Detection Method for Wolfberry Moisture Grade Using Hyperspectral Imaging Technology

In situ Raman quantitative monitoring of methanogenesis: Culture experiments of a deep-sea cold seep methanogenic archaeon.

Gas production from several metabolic pathways is a necessary process that accompanies the growth and central metabolism of some microorganisms. However, accurate and rapid nondestructive detection of gas production is still challenging. To this end, gas chromatography (GC) is primarily used, which requires sampling and sample preparation. Furthermore, GC is expensive and difficult to operate. Several researchers working on microbial gases are looking forward to a new method to accurately capture the gas trends within a closed system in real-time. In this study, we developed a precise quantitative analysis for headspace gas in Hungate tubes using Raman spectroscopy. This method requires only a controlled focus on the gas portion inside Hungate tubes, enabling nondestructive, real-time, continuous monitoring without the need for sampling. The peak area ratio was selected to establish a calibration curve with nine different CH4-N2 gaseous mixtures and a linear relationship was observed between the peak area ratio of methane to nitrogen and their molar ratios (A(CH4)/A(N2) = 6.0739 × n(CH4)/n(N2)). The results of in situ quantitative analysis using Raman spectroscopy showed good agreement with those of GC in the continuous monitoring of culture experiments of a deep-sea cold seep methanogenic archaeon. This method significantly improves the detection efficiency and shows great potential for in situ quantitative gas detection in microbiology. It can be a powerful complementary tool to GC.

Near surface residual stress detection of metallic materials using the high order ultrasonic nonlinear coefficient

Because residual stress seriously affects the mechanical properties of metallic components, in the study the nonlinear property of LCR wave is studied and the near surface residual stress within a certain depth beneath the component surface is nondestructively detected for the safe service of metal components. The stress detection system based on LCR wave is constructed using the oblique incident method with one-in-one-out mode. The high order relative nonlinear coefficients, β′ and δ′, during 45 steel tensile process are measured to investigate the correlation between nonlinear property of ultrasonic and stress. Two pairs of relative nonlinear coefficients monotonically increase and the stress sensitivity of δ′ is better than β′. In order to eliminate the sensitivity difference, the comprehensive nonlinear parameter is proposed. Three stress detection models based on experimental data are established and the error of residual stress evaluation are all below 4%, this indicates the nonlinear characteristics of LCR wave can be used to detect the stress level within a certain depth beneath the component surface. So the high-order ultrasonic nonlinear coefficients of LCR wave provides an effective method of rapid non-destructive detection the near surface residual stress of metallic material.

Rapid Detection of Tea Polyphenols in Fresh Tea Leaves Based on Fusion of Visible/Short-Wave and Long-Wave near Infrared Spectroscopy and Its Device Development

Tea polyphenols are considered as an important indicator of tea quality. Rapid detection of tea polyphenol content plays a valuable role for tea breeding and quality inspection during tea production. In this work, a portable rapid non-destructive detection device of tea polyphenols in fresh tea leaves was developed, which integrated the fusion technology of visible/short-wave (400–1050 nm) and long-wave (1000–1650 nm) near-infrared spectroscopy (Vis/NIR). Experimental results indicated that the spectra within the overlapping region (1000–1050 nm) were assembled by applying the spectral data fusing method. Followed by spectral data preprocessing with the Savitzky–Golay smoothing (SG) method, least squares support vector regression (LS–SVR) models were established for detecting the tea polyphenol content of fresh tea leaves. Based on the fused Vis/NIR spectra (dual-band), the correlation coefficient of calibration (RC), root mean square error of calibration (RMSEC), correlation coefficient of prediction (RP), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) reached 0.976, 0.679%, 0.893, 0.897%, and 2.230, respectively, which were better than the visible/short-wave or long-wave near infrared spectral data (single-band). The sensitive spectral wavebands of tea polyphenols extracted using the random frog (RF) algorithm were distributed in 402–448 nm, 555–600 nm, 810–1042 nm, 1056–1103 nm, 1219–1323 nm, 1406–1416 nm, and 1499–1511 nm. This demonstrated that the prediction of tea polyphenol content using fused spectral data combined with the LS–SVR model depended on various functional groups such as auxochromes, chromogenic groups, and hydrogen-containing groups. The proposed device is capable of non-destructive detection of tea polyphenol content in fresh tea leaves, which can provide effective technical support for tea breeding and tea leaf quality control.

Diagnosis of Osteoarthritis at an Early Stage via Infrared Spectroscopy Combined Chemometrics in Human Serum: A Pilot Study

Methods applied for early diagnosis of osteoarthritis (OA) are limited. Early prevention and treatment can effectively reduce the pain of OA patients and save costs. The present study aimed to develop a rapid non-destructive detection method for early diagnosis of OA by evaluating infrared (IR) spectroscopy combined chemometrics. Our cohort consisted of (a) 15 patients with osteoarthritis (OA) and (b) 10 without clinical signs of the disease and they were used as controls. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy was used to investigate serum samples (50 µL) collected from these patients. A supervised classification algorithm namely discriminant analysis (DA) was applied to evaluate the diagnostic accuracy spectral processing and chemometrics analysis allowed for detecting spectral biomarkers that discriminated the two cohorts. About 250 infrared spectra were statistically important for separating the groups. Peaks at 1000 cm−1 in OA serum were associated mainly with C–O stretching vibration associated with the changes in the proteoglycan contents previously reported in OA. A good overall classification accuracy of 74.47% was obtained from the DA model. Our findings indicated that this discriminating model, which incorporated the ATR-FTIR spectrum, could provide a rapid and cost-effective blood test, thus facilitating the early diagnosis of human OA.