Normal Variation Research Articles

Kidney Dimensions and its Correlation with Anthropometric Parameters in Healthy North Indian Adults

Background: Knowledge of kidney size is important in the assessment of kidney function. Changes in kidney size can occur in various kidney diseases due to different causes, hence knowledge of normal kidney dimensions in a population is crucial for diagnosis, follow-up and prognostication. While data from other parts of the world does not apply to the Indian population due to differences in ethnicity, diet and body sizes, and there is also a lack of standardized data on normal kidney sizes in healthy Indian adults. Materials and Methods: Kidney dimensions from 600 healthy adult volunteers ranging between 20 and 70 years of age were measured with sonography by a single radiologist. Differences in dimensions between men and women, and right and left kidney were analyzed. Finally, kidney sizes were correlated with anthropometric variables such as weight, age, body surface area (BSA), height and body mass index. Estimated glomerular filtration rate (eGFR) was correlated with kidney length and renal parenchymal volume (RPV). Results: The mean kidney length of the whole cohort, irrespective of gender was found to be 9.6 ± 0.7 cm on the right and 9.9 ± 0.7 cm on the left. Mean kidney length in males was significantly more as compared to females on both sides. Both the kidney length and RPV were significantly associated with BSA, weight and height (in that order) in females, whereas in males, kidney length and RPV best correlated with height, BSA and weight (in that order). In both sexes, there was a significant negative correlation between age and kidney length, RPV. eGFR had a significant positive correlation with kidney length and RPV in the cohort. Conclusion: Normal sonographic mean kidney length was 9.6±0.7 cm and 9.9±0.7 cm on the right and left sides respectively in healthy North Indian population, with the left kidney being larger than the right in all dimensions (length, width, thickness and RPV). Kidney sizes in males were found to be larger than females. Correlation with anthropometric parameters in our study, emphasizes the need to give due consideration to normal variations in kidney sizes with age, gender, height, weight and BSA to differentiate between a normal and a pathologically small or large kidney.

CSF-venous fistulas associated with traumatic spinal pseudomeningoceles.

CSF-venous fistulas (CVFs) are a common cause of spontaneous intracranial hypotension. These fistulas usually occur without any preceding major trauma, surgery, or other iatrogenic cause. Occasionally, patients have a history of minor trauma, though such cases are usually still considered spontaneous. Little is known about predisposing factors that cause patients to develop spontaneous CVFs. Most patients with CVFs have multiple meningeal diverticula on spine imaging, and fistulas usually arise in association with a diverticulum. In the vast majority of cases, the culprit diverticulum from which the CVF arises is atraumatic in origin, presumably on the spectrum of normal variation in spinal anatomy. Here, we present two cases of CVFs that arose in association with posttraumatic pseudomeningoceles. To our knowledge, this phenomenon has not yet been reported, and it potentially represents a novel etiology for CVFs that furthers understanding of their pathogenesis.

基于透射光谱技术的马铃薯种薯黑心病检测研究

Black heart disease is one of the screening indicators of seed potatoes, which has a serious impact on the quality and yield of potato, and at present there are fewer non-destructive testing methods for internal defects of seed potatoes. This paper aims to utilize non-destructive testing technology to quickly identify qualified and black hearted seed potatoes, and then to protect yield and food security. In this paper, transmission spectroscopy system was utilized to collect the spectral data of 104 qualified seed potatoes and 104 black hearted seed potatoes in 450~940 nm band. Subsequently, four algorithms, namely Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and First-order Derivative (FD), were utilized to pre-process the seed potatoes spectral data to improve the data noise ratio. Feature wavelength extraction was made using Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) to enhance the sample data characteristics and improve the model interpretability. The construction of classification models for qualified and black hearted seed potatoes relied on two deep learning techniques, Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN), which were trained and tested for the feature bands respectively. The experimental results showed that SG-CARS-CNN was the optimal combination of classification algorithms, and the classification accuracies of both the training set and the test set reached 100%, which improved the accuracy of the test set by 3.85% compared with that of the traditional machine learning algorithms, and provided an accurate method for the rapid screening of qualified seed potatoes.

Authentication of the shelf-life of NPH injectable insulins samples using NIR and MS spectral fingerprints

Insulin is essential for regulating glucose levels, and its hormonal deficiency leads to diabetes. Treatment involves the use of different types of insulin tailored to individual needs. Properly stored and transported Neutral Protamine Hagedorn (NPH) insulin remains effective and stable, highlighting the importance of quality control to ensure its efficacy and patient safety. This study aims to develop an analytical method to evaluate the shelf-life of NPH injectable insulin samples while preserving packaging integrity. The approach consisted of analyzing intact insulin samples, with and without expired shelf-life dates, using near-infrared spectroscopy (NIR) obtained from two instruments: a benchtop and a portable device. These measurements were coupled with chemometric methods, specifically single-class classification algorithms. The classifiers considered were Data Driven-Soft Independent Modeling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OC-PLS). For benchtop NIR, the DD-SIMCA method, with Savitzky-Golay smoothing preprocessing (41-point window) and baseline offset (BO), achieved excellent results, reaching 100% sensitivity, specificity, and efficiency in both training and test sets. As for portable NIR, the most effective methods were DD-SIMCA and OC-PLS, using datasets preprocessed with Savitzky-Golay smoothing with first derivative, second-order polynomial (SGD-1st-2nd), and 9-point windows combined with Standard Normal Variate (SNV), and Savitzky-Golay smoothing filters (SGS) using second-order polynomial and 7-point windows combined with baseline offset (BO) and Multiplicative Scattering Correction (MSC). The results were corroborated by mass spectrometry, correlating the structural changes of degraded insulin with NIR data. The proposed methodology is a promising tool for non-destructive, rapid, and cost-effective authentication of the preservation state of NPH insulin, aligning with green chemistry principles and allowing for in-situ evaluation, thereby preserving medication integrity.

Innovative strategies for protein content determination in dried laver (Porphyra spp.): Evaluation of preprocessing methods and machine learning algorithms through short-wave infrared imaging

In this study, we explored the application of Short-Wave Infrared (SWIR) hyperspectral imaging combined with Competitive Adaptive Reweighted Sampling (CARS) and advanced regression models for the non-destructive assessment of protein content in dried laver. Utilizing a spectral range of 900–1700 nm, we aimed to refine the quality control process by selecting informative wavelengths through CARS and applying various preprocessing techniques (standard normal variate [SNV], Savitzky-Golay filtering [SG], Orthogonal Signal Correction [OSC], and StandardScaler [SS]) to enhance the model's accuracy. The SNV-OSC-StandardScaler- Support vector regression (SVR) model trained on CARS-selected wavelengths significantly outperformed the other configurations, achieving a prediction determination coefficient (Rp2) of 0.9673, root mean square error of prediction of 0.4043, and residual predictive deviation of 5.533. These results highlight SWIR hyperspectral imaging's potential as a rapid and precise tool for assessing dried laver quality, aiding food industry quality control and dried laver market growth.

Separating Surface Reflectance from Volume Reflectance in Medical Hyperspectral Imaging.

Hyperspectral imaging has shown great promise for diagnostic applications, particularly in cancer surgery. However, non-bulk tissue-related spectral variations complicate the data analysis. Common techniques, such as standard normal variate normalization, often lead to a loss of amplitude and scattering information. This study investigates a novel approach to address these spectral variations in hyperspectral images of optical phantoms and excised human breast tissue. Our method separates surface and volume reflectance, hypothesizing that spectral variability arises from significant variations in surface reflectance across pixels. An illumination setup was developed to measure samples with a hyperspectral camera from different axial positions but with identical zenith angles. This configuration, combined with a novel data analysis approach, allows for the estimation and separation of surface reflectance for each direction and volume reflectance across all directions. Validated with optical phantoms, our method achieved an 83% reduction in spectral variability. Its functionality was further demonstrated in excised human breast tissue. Our method effectively addresses variations caused by surface reflectance or glare while conserving surface reflectance information, which may enhance sample analysis and evaluation. It benefits samples with unknown refractive index spectra and can be easily adapted and applied across a wide range of fields where hyperspectral imaging is used.

Visualization and analysis of medically relevant tandem repeats in nanopore sequencing of control cohorts with pathSTR.

The lack of population-scale databases hampers research and diagnostics for medically relevant tandem repeats and repeat expansions. We attempt to fill this gap using our pathSTR web tool, which leverages long-read sequencing of large cohorts to determine repeat length and sequence composition in a healthy population. The current version includes 1040 individuals of the 1000 Genomes Project cohort sequenced on the Oxford Nanopore Technologies PromethION. A comprehensive set of medically relevant tandem repeats was genotyped using STRdust and LongTR to determine the tandem repeat length and sequence composition. PathSTR provides rich visualizations of this dataset and the feature to upload one's data for comparison along the control cohort. We demonstrate the implementation of this application using data from targeted nanopore sequencing of a patient with Myotonic Dystrophy type 1. This resource will empower the genetics community to get a more complete overview of normal variation in tandem repeat length and sequence composition and, as such, enable a better assessment of rare tandem repeat alleles observed in patients.

Identification and quantification of goat milk adulteration using mid-infrared spectroscopy and chemometrics

The fraudulent adulteration of goat milk with cheaper and more available milk of other species such as cow milk is occurrence. The aims of the present study were to investigate the effect of goat milk adulteration with cow milk on the mid-infrared (MIR) spectrum and further evaluate the potential of MIR spectroscopy to identify and quantify the goat milk adulterated. Goat milk was adulterated with cow milk at 5 different levels including 10%, 20%, 30%, 40%, and 50%. Statistical analysis showed that the adulteration had significant effect on the majority of the spectral wavenumbers. Then, the spectrum was preprocessed with standard normal variate (SNV), multiplicative scattering correction (MSC), Savitzky-Golay smoothing (SG), SG plus SNV, and SG plus MSC, and partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) were used to establish classification and regression models, respectively. PLS-DA models obtained good results with all the sensitivity and specificity over 0.96 in the cross-validation set. Regression models using raw spectrum obtained the best result, with coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) of cross-validation set were 0.98, 2.01, and 8.49, respectively. The results preliminarily indicate that the MIR spectroscopy is an effective technique to detect the goat milk adulteration with cow milk. In future, milk samples from different origins and different breeds of goats and cows should be collected, and more sophisticated adulteration at low levels should be further studied to explore the potential and effectiveness of milk mid-infrared spectroscopy and chemometrics.

Smart classification of organic and inorganic pineapple juice using dual NIR spectrometers combined with chemometric techniques

The global demand for organic foods, driven by health benefits and consumer preferences, necessitates reliable methods for distinguishing organic products from their inorganic counterparts. This study investigates the application of dual handheld near infrared (NIR) spectroscopy devices, SCiO and Tellspec, combined with chemometric techniques for the nondestructive differentiation of organic and inorganic pineapple juices. The objective was to establish a rapid and robust method to differentiate organic pineapple juice from inorganic juice using unique spectral data from the two devices. Eighty-four pineapple juice samples were analyzed with preprocessing techniques, including mean centering, multiplicative scatter correction, standard normal variate, first derivative, and second derivative applied to the spectral data. Partial least squares discriminant analysis (PLS-DA) was employed for classification, and variable importance in projection (VIP) was used for optimal wavelength selection. The results demonstrated that the Tellspec scanner, particularly with second derivative preprocessing, achieved high accuracy in differentiating organic from inorganic pineapple juice. The fusion of data from both SCiO (740–1070 nm) and Tellspec (900–1700 nm) scanners, without preprocessing, coupled with the PLS-DA model, achieved perfect classification accuracy, sensitivity, and specificity (100 %) in both training and testing sets. This study highlights the potential of integrating dual handheld NIR spectroscopy with chemometrics to effectively and accurately classify organic and inorganic pineapple juices. The findings support using these advanced techniques for quality assurance and authentication in the food industry.

Clinical, radiological and dynamic CT results of scapholunate intercarpal ligamentoplasty for scapholunate dissociation

A clinical, radiological and four-dimensional computed tomography (4DCT) assessment of the outcomes of scapholunate intercarpal ligamentoplasty (SLICL) was done with a minimum follow-up of 2 years. Twenty-nine patients (23 men and 6 women) with a mean age of 40 years (22–57) who had chronic scapholunate dissociation were treated with the SLICL procedure. There were 18 cases of dynamic instability and 11 of static instability. The patients were evaluated with a mean follow-up of 61 months (24–94). SLICL significantly reduced pain and increased grip strength and wrist function. On radiographs, the mean static and dynamic scapholunate gaps as well as the scapholunate and radiolunate angles improved significantly. The dorsal scaphoid displacement was always corrected. 4DCT after surgery provided a more precise analysis of the SLICL’s effectiveness at restoring intracarpal alignment. Correction of the DISI deformity and dorsal scaphoid displacement was confirmed. SLICL restored a normal variation in the scapholunate gap (range value) during radioulnar deviation movement without systematically reducing the distance between the bones (mean and maximum values) which remained pathological in wrists with static instability but not in those with dynamic instability. At the final follow-up, no patients had signs of osteoarthritis due to ScaphoLunate Advanced Collapse. Level of evidenceIII.

Detection of early bruises in plum using hyperspectral imaging combination with machine learning algorithm

Early detection of plum bruising is important in the online postharvest quality sorting process. This paper explores the rapid detection of bruises in plum at five stages (1, 3, 6, 24, and 48 h after bruising) using hyperspectral imaging system. Spectral preprocessing was performed using five methods (unit vector normalization, multiplicative scattering correction, standard normal variate, detrending, and baseline). The support vector machine was established to discriminate the spectral samples of healthy and bruised plums at stage 1 on full wavelengths. The results indicated that the spectral data pretreated by multiple scattering correction yielded better results. The characteristic wavelengths of the spectra were selected by six algorithms (random frog, competitive adaptive reweighted sampling, variable combination population analysis, successive projection algorithm, uninformative variable elimination and bootstrapping soft shrinkage). Subsequently, the support vector machine was established at stage 1 based on these selected characteristic wavelengths. Comparing the results of models, the support vector machine based on the characteristic wavelengths selected by competitive adaptive reweighted sampling generated a satisfied effect with an accuracy of 95% for the calibration set and 98% for the prediction set. This model was selected for bruise detection in plums at the residual stage. The characteristic wavelength grayscale images selected based on the model were used to successfully visualize the plum bruise regions in five stages using minimum noise fraction transformation and the principal component analysis method. The results showed that the hyperspectral imaging technique combined with machine learning algorithm could be used to identify plum bruises at different stages. This study contributes to the development of an online detection system for bruises in plum.

Assessment and Management of Maxillary Labial Frenum-A Scoping Review.

The maxillary labial frenum (MLF) is a soft tissue fold connecting the upper lip to the alveolar process. Abnormal attachment can cause periodontal, functional, and esthetic problems. Differential diagnosis is important and can prevent unnecessary interventions. This study aims to summarize the current evidence on the assessment and management of abnormal MLF. A thorough review of the literature was conducted. Five online databases were searched for relevant peer-reviewed human studies. Article screening and data extraction were performed independently by two reviewers using predefined inclusion/exclusion criteria. Information about article type, study design, participants' characteristics, interventions, and outcomes was extracted and synthesized. 52 articles met the review criteria. MLF is a dynamic structure characterized by a wide normal morphological variation. MLF assessment in infants has not been standardized. Studies in pre-adolescents reported a change in the thickness and position of the MLF observed over time, resulting in a lower prevalence of abnormal MLF morphology. Studies in adolescents and adults reported variable differential diagnosis criteria. Lasers appear as the most advantageous frenectomy modality. There is a need for more objective MLF diagnostic protocols and treatment guidelines, which could prevent unnecessary surgical interventions.

Integrating Hyperspectral Reflectance and Physiological Parameters to Detect Urban Tree Stress: A Study of Drought and Simulated Acid Rain

With urbanization and climate change worsening, urban trees are constantly exposed to environmental stress. To enhance the functionality and health of trees, it is crucial to rapidly and non-destructively detect and respond to tree stress. Research utilizing hyperspectral characteristics for detecting various stresses has recently been actively pursued. This study conducted comparative analysis using various leaf physiological parameters (chlorophyll content, chlorophyll fluorescence, leaf water, and gas exchange status) and hyperspectral data (VIS: visible ray; SWIR: short-wave infrared) to diagnose stress in Prunus yedoensis, commonly grown urban trees, by subjecting them simultaneously to different stresses (drought and simulated acid rain). The findings suggest that hyperspectral reflectance proved more responsive in identifying stress compared to the physiological parameters. Initially, VIS was more effective in detecting two stress responses than SWIR through a classification model (PLS-DA: partial least squares-discriminant analysis). Although SWIR initially faced challenges in simulated acid rain stress detection, spectral preprocessing (SNV: standard normal variate, + S.G 2nd: Savitzky–Golay 2nd derivative) enhanced its stress classification accuracy. Over time, the SWIR bands (1437 nm, 1667 nm, and 1949 nm) exhibited characteristics (such as moisture detection) more closely aligned with stress responses compared to VIS, as determined through PCA (principal component analysis). Hyperspectral reflectance also revealed the potential to measure chlorophyll fluorescence (Fo: minimum fluorescence). Building upon the foundational data of this study, the future potential of diagnosing urban tree stress using portable spectrometers is strong.

Discrimination of Fungicide-Contaminated Lettuces Based on Maximum Residue Limits Using Spectroscopy and Chemometrics

The fast and effective monitoring of agrochemical residues is essential for assuring food safety, since many agricultural products are sprayed with pesticides and commercialised without waiting for the pre-harvest interval. In this study, we investigated the use of spectral reflectance combined with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to evaluate the discrimination of fungicide-contaminated lettuces, considering three maximum residue limits (MRLs) [3.5, 5, and 7 mg carbon disulphide (CS2) kg−1]. The non-systemic Mancozeb fungicide (dithiocarbamate) was adopted in this research. Spectral reflectance (Vis/NIR) was measured by a hand-held spectrometer connected to a clip probe with an integrating sphere. The lettuce spectra were pre-treated (centring, standard normal variate, and first derivative) before data processing. Our findings suggest that PCA recognised inherent similarities in the fungicide-contaminated lettuce spectra, categorising them into two distinct groups. The PLS-DA models for all MRLs resulted in high accuracy levels, with correct discriminations ranging from 94.5 to 100% for the external validation dataset. Overall, our study demonstrates that spectroscopy combined with discriminating methods is a promising tool for non-destructive and fast discrimination of fungicide-contaminated lettuces. This methodology can be used in industrial food processing, enabling large-scale individual analysis and real-time decision making.

Influence of the peel on online detecting soluble solids content of pomelo using Vis-NIR spectroscopy coupled with chemometric analysis

The application of Visible-near infrared spectroscopy (Vis-NIRs) to internal quality detection of pomelo with large sizes and thick peels is still challenging. Considering that the peel has a large mass ratio in pomelo and a great added value in industrial processing, a postharvest processing mode of grading after peeling without affecting consumption of pomelos may be an approach to avoid adverse influence of peel on soluble solids content (SSC) evaluation. In this study, we investigated the performance variation of Vis-NIRs for SSC online determination of pomelos with and without peel for the first time. An online detection system for pomelo spectra acquisition was developed, and the spectral characteristic difference of the intact and peeled pomelos was analyzed. Subsequently, the performance of calibration models was gradually improved and comparatively analyzed by various spectral processing and wavelength selection methods. Furthermore, convolutional neural network (CNN) was utilized to explore its ability for feature extraction. The results showed that combined with standard normal variate (SNV) and second order detrending and changeable-size moving window algorithms, the partial least squares regression (PLSR) model using spectra of peeled pomelos achieved the best prediction results. Specifically, the model attained a determination coefficient of prediction (Rp2) of 0.88, a root mean square error of prediction (RMSEP) of 0.294%, and a residual predictive deviation (RPD) value of 2.57. This study demonstrates that the peel has a significantly negative effect on the prediction performance and the CNN could be an alternative to conventional PLSR method. Our work may open new avenues for the internal quality assessment of agro-products with complex tissue structure.

The Pediatric Ankle: Normal Variations and Maturation-Dependent Pathology.

The pediatric ankle can present a broad range of normal variation and pathology unique to certain stages of development. Understanding the expected age ranges of ossification and fusion about the ankle is essential to provide accurate diagnoses regarding skeletal integrity. This conclusion has been well characterized radiographically and is supported by cadaveric research.The range of appearances on magnetic resonance imaging has also been well described. Knowledge about the structure of the periosteum and perichondrium aids in image interpretation as well as explaining typical injury patterns. The expected appearance of the physis and regional bone marrow signal is also of utmost importance.Ultrasonography is a valuable tool in pediatric musculoskeletal imaging but is limited when there is concern for intra-articular pathology. Computed tomography tends to be reserved for preoperative evaluation. We describe normal variation and maturation-dependent pathology of the pediatric ankle with an emphasis on imaging considerations.

Prenatal diagnosis and genetic counseling of a de novo 10q11.21q11.23 duplication associated with a normal phenotype.

Copy number variants (CNVs) are an important source of normal and pathogenic genome variations. Unbalanced chromosome abnormalities are either gains or losses of large genomic regions that do not or only minimally clinically affect the individual. Noninvasive prenatal testing (NIPT) is widely used in the screening of common fetal chromosome aneuploidy. One example is the duplication of 10q11.21q11.23, which includes the 10q11.2 region. This region contains a complex set of low-copy repeats that may lead to various genomic alterations through non-allelic homologous recombination. In this report, we present a case of a de novo 10q11.21q11.23 duplication with a normal phenotype. This case may be helpful for prenatal diagnosis and genetic counseling. A combination of NIPT, prenatal ultrasound, karyotype analysis, copy number variation sequencing, and genetic counseling is helpful for the prenatal diagnosis of CNVs.

Host and environmental factors shape upper airway microbiota and respiratory health across the human lifespan

Our understanding of the normal variation in the upper respiratory tract (URT) microbiota across the human lifespan and how these relate to host, environment, and health is limited. We studied the microbiota of 3,104 saliva (<10 year-olds)/oropharynx (≥10 year-olds) and 2,485 nasopharynx samples of 3,160 Dutch individuals 0-87 years of age, participating in a cross-sectional population-wide study (PIENTER-3) using 16S-rRNA sequencing. The microbiota composition was strongly related to age, especially in the nasopharynx, with maturation occurring throughout childhood and adolescence. Clear niche- and age-specific associations were found between the microbiota composition and host/environmental factors and health outcomes. Among others, social interaction, sex, and season were associated with the nasopharyngeal microbial community. By contrast, the oral microbiota was more related to antibiotics, tobacco, and alcohol use. We present an atlas of the URT microbiota across the lifespan in association with environment and health, establishing a baseline for future research.

Raman spectroscopy-based authentication of powder goat milk adulteration with cow milk

This study introduces, for the first time, the utilization of a handheld Raman spectrometer in conjunction with Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) for the authentication of powdered goat milk (target class) against adulteration with powdered cow milk. Since the representativeness of the target class is the first critical step to construct a model without overfitting or underfitting, especially when dealing with a few numbers of target samples, three distinct validation strategies (approaches 1, 2, and 3, respectively), incorporating no partitioning of the target samples, Procrustes cross-validation, and Kennard-Stone-based partitioning, were assessed. Moreover, the spectral data underwent preprocessing techniques including offset correction (OFF), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), and Savitzky-Golay derivative (SGD). Achieving a correct classification rate of 100% for all studied samples using MSC for approaches 1 and 2, and SGD for approach 3, underscores the robustness of Raman spectroscopy in furnishing pertinent analytical insights for the authentication of pure powdered goat milk, with a significance level of 0.01. This methodology furnishes distinct spectral signatures associated with the macromolecular composition, particularly fats, proteins, carbohydrates, and carotenoids, facilitating the detection of adulterations as low as 1% w/w of cow milk added to powdered goat milk. Thus, the proposed method offers a rapid and reliable on-site screening tool, consonant with the principles of green food analysis, especially advantageous in situations where identifying the type and/or nature of the adulterants is not feasible.

Novel authentication of African geographical coffee types (bean, roasted, powdered) by handheld NIR spectroscopic method

African coffee is among the best traded coffee types worldwide, and rapid identification of its geographical origin is very important when trading the commodity. The study was important because it used NIR techniques to geographically differentiate between various types of coffee and provide a supply chain traceability method to avoid fraud. In this study, geographic differentiation of African coffee types (bean, roasted, and powder) was achieved using handheld near-infrared spectroscopy and multivariant data processing. Five African countries were used as the origins for the collection of Robusta coffee. The samples were individually scanned at a wavelength of 740–1070 nm, and their spectra profiles were preprocessed with mean centering (MC), multiplicative scatter correction (MSC), and standard normal variate (SNV). Support vector machines (SVM), linear discriminant analysis (LDA), neural networks (NN), random forests (RF), and partial least square discriminate analysis (PLS-DA) were then used to develop a prediction model for African coffee types. The performance of the model was assessed using accuracy and F1-score. Proximate chemical composition was also conducted on the raw and roasted coffee types. The best classification algorithms were developed for the following coffee types: raw bean coffee, SD-PLSDA, and MC + SD-PLSDA. These models had an accuracy of 0.87 and an F1-score of 0.88. SNV + SD-SVM and MSC + SD-NN both had accuracy and F1 scores of 0.97 for roasted coffee beans and 0.96 for roasted coffee powder, respectively. The results revealed that efficient quality assurance may be achieved by using handheld NIR spectroscopy combined with chemometrics to differentiate between different African coffee types according to their geographical origins.