Successful Discrimination Research Articles

Generalized identity in a successive matching-to-sample procedure in rats: Effects of number of exemplars and a masking stimulus.

Two experiments examined the emergence of generalized identity matching in rats using a successive discrimination procedure with olfactory stimuli. Trials consisted of the presentation of two odors separated by a 1-s interstimulus interval. Responses during the second odor presentation were reinforced only if the two odors were identical. In Experiment 1, rats were trained with two odors and then exposed to sessions that included unreinforced probe trials with novel odors. There was evidence of higher response rates on matching probe trials in some rats, but matching did not approach baseline levels. Additional training with four exemplars produced transfer to novel odors that was equivalent to baseline levels. Experiment 2 tested the possibility that detection of stimulus change, rather than generalized identity, was responsible for the transfer seen in Experiment 1. Thus, a masking odor was inserted during the 1-s interstimulus interval so that stimulus change occurred on all trials. Although response rates on probe trials were lower than baseline rates, above chance transfer to novel stimuli was still observed in four of the five animals tested. These findings support the hypothesis that transfer of matching to novel odors in this successive matching-to-sample paradigm is based on a generalized identity relation.

Detecting Counterfeit Brandies.

A hypothesis-free sensor array (optoelectronic tongue) composed of an anionic, a cationic and two neutral poly(para-aryleneethynylene)s (PAE) at pH 3, 7 and 13 discriminate more than 30 spirits (including brandy, Branntwein, Cognac, Spirituose, and Weinbrand). Counterfeits (made by mixing of low-quality spirits and caramel colour) and different batches of identical brands of brandies are discriminated. The sensor array works without sample preparation or great instrumental cost, and is superior to conventional methods with respect to sample need (10-20 μL), time and effort. The discrimination stems from differential fluorescence quenching of the PAE-array by the complex mixture of the beverages' colourants, from the oak barrels or added caramel colour. The collected quenching data were analysed by linear discriminant analysis (LDA) and principal component analysis (PCA) to achieve successful discrimination.

Nanozyme Sensor Arrays for Detecting Versatile Analytes from Small Molecules to Proteins and Cells.

Nanozymes have emerged as promising alternatives to overcome the high cost and low stability of natural enzymes. Nanozymes with peroxidase-like activities have been studied to construct versatile biosensors by using specific biorecognition ligands (such as enzymes, antibodies, and aptamers) or molecularly imprinted polymers (MIPs). However, the use of bioligands compromises the high stability and low cost promise of nanozymes, while the MIPs may not be applicable to multiplex detection. To address these limitations, here we constructed the nanozyme sensor arrays based on peroxidase-like Pt, Ru, and Ir nanozymes. The cross-reactive nanozyme sensor arrays were successfully used for the detection of biothiols and proteins as well as the discrimination of cancer cells because of the differential nonspecific interactions between the components of the sensor arrays and the analytes. The usefulness of the nanozyme sensor arrays was further validated by the detection of blind unknown samples, where 28 of 30 biothiols and 42 of 45 proteins were correctly identified. Moreover, the practical application of the nanozyme sensor arrays was demonstrated by the successful discrimination of biothiols in serum and proteins in human urine.

Plasmonic Nanotrough Networks for Scalable Bacterial Raman Biosensing.

We demonstrate a novel approach for fabricating surface enhanced Raman scattering (SERS) substrates for single bacterial biosensing based on Ag cylindrical nanotrough networks (CNNs). This approach is developed with large scalability by leveraging a cellulose nanofiber template fabrication via facile electrospinning. Specifically, a concave nanotrough structure consisting of interconnected concave Ag nanoshells is demonstrated by depositing a thin layer of Ag atop a sacrificial electrospun nanofiber template and then completely removing the cellulose core in water. Our investigations of the scattering properties and SERS performances of single isolated Ag nanotroughs of different diameters reveal that nanotrough-based substrates provide tunable optical responses and enhanced SERS intensities. Further, Ag CNNs are fabricated in highly interconnected networks that yield reproducible SERS signals for molecular monolayers and whole bacterial cells, enabling rapid spectral discrimination between different bacterial strains. Finally, by performing principal component analysis on a large number of measured SERS spectra (40 spectra per bacterium), we demonstrate successful spectral discrimination between two types of Escherichia coli ( E. coli) bacteria, that is, E. coli K12 with its derivative E. coli DH 5α and E. coli BL21(DE3). The demonstrated cost-effective substrates feature several advantages over conventional SERS substrates including environmentally friendly and scalable fabrication compatible with versatile devices and provide an alternative approach to rapid SERS detection and screening of biochemicals.

Raman spectroscopy coupled with chemometric methods for the discrimination of foreign fats and oils in cream and yogurt

The adulteration of milk fat in dairy products with cheaper non-milk based fats or oils is frequently encountered in the dairy industry. In this study, Raman spectroscopy with chemometric was used for the discrimination of foreign fats and oils in milk cream and yogurt. Firstly, binary mixtures of cream and oils (corn and sunflower oil), and vegetable fat blends which are potentially or currently used by the dairy industry were prepared. All fat or oil samples and their binary mixtures were examined by using Raman spectroscopy. Then, fat content of skim milk was adjusted to 3% (w/w) by the milk fat, external oils or fats, and binary mixtures, and was used in yogurt production. The lipid fraction of yogurt was extracted and characterized by Raman spectroscopy. The spectral data were then pre-processed and principal component analysis (PCA) was performed. Raman spectral data showed successful discrimination for about the source of the fats or oils. Temperature effect was also studied at six different temperatures (25, 30, 40, 50, 60 and 70 °C) in order to obtain the best spectral information. Raman spectra collected at higher temperatures were more intense. Obtained results showed that the performance of Raman spectroscopy with PCA was very promising and can be expected to provide a simple and quick way for the discrimination of foreign fats and oils in both milk cream and yogurt. Fermentation and yogurt processing affected clustering of fat samples by PCA, probably depending on some lipolysis or production of new products that can affect the Raman scattering. However, those changes did not affect differentiation of samples by Raman spectroscopy.

Dairy products discrimination according to the milk type using an electrochemical multisensor device coupled with chemometric tools

This study shows the potential application of a potentiometric electronic tongue coupled with a lab-made DataLogger device for the classification of dairy products according to the type of milk used in their production, i.e., natural, fermented and UHT milk. The electronic tongue device merged a commercial pH electrode and 15 lipid/polymeric membranes, which were obtained by a drop-by-drop technique. The potentiometric signal profiles gathered from the 16 sensors, during the analysis of the 11 dairy products (with ten replicate samples), together with principal component analysis showed that dairy samples could be naturally grouped according to the three types of milk evaluated. To further investigate and verify this capability, a linear discriminant analysis together with a simulated annealing variable selection algorithm was also applied to the electrochemical data, which were randomly split into two datasets, one used for model training and internal-validation using a repeated K-fold cross-validation procedure (with 64% of the data); and the other for external validation purposes (containing the remaining 36% of the data). The multivariate supervised strategy used allowed establishing a classification model, based on the potentiometric information of four sensor lipid membranes, which enabled achieving a successful discrimination rate of 100% for both internal- and external-validation processes. The demonstrated versatility of the built electronic tongue for discriminating dairy products according to the type of milk used in their production combined with its simplicity, low-cost and fast time analysis may envisage a possible future application in dairy industry.

Otolith‐Chemistry‐Based Discrimination of Wild‐ and Hatchery‐Origin Steelhead across the Lake Michigan Basin

AbstractEffective conservation and management of mixed‐stock fisheries depends upon accurate stock identification of individual fish. We evaluated the utility of otolith chemical analysis as an approach to classify juvenile, premigratory steelhead Oncorhynchus mykiss collected in tributaries of Lake Michigan as either wild or hatchery origin. Two primary hypotheses proposed were that otolith chemistry can be used (1) to classify juvenile, hatchery‐origin steelhead to the correct hatchery of origin and (2) to correctly classify stream‐collected juvenile steelhead as wild or hatchery origin. Using laser‐ablation inductively coupled plasma mass spectrometry, we analyzed the concentrations of seven elements (using 25Mg, 55Mn, 65Cu, 66Zn, 88Sr, 137Ba, 208Pb) in the otoliths of juvenile steelhead collected from five hatcheries and 25 streams in the Lake Michigan basin. When discriminating among hatcheries, only 4.41% of fish misclassified to an alternate hatchery when subjected to the best random forest classification algorithm that included all elements as predictor variables. Distinct chemical signatures between fish of wild and hatchery origin supported 100% classification accuracies of known‐wild, age‐0 steelhead as wild origin for 19 of the 25 streams sampled. Misclassification of wild, age‐0, and hatchery‐origin fish, which tended to occur for streams that were located in close proximity to the hatchery, never exceeded 3.4% for a given stream. Our findings demonstrate highly successful discrimination of hatchery‐origin and wild juvenile steelhead across a broad geographic range. Applying the classification algorithms developed herein to unknown‐origin steelhead provides the ability to infer survival of year‐classes from specific hatcheries. Further, the ability to differentiate hatchery‐ and wild‐origin fish will assist in stock assessment efforts allowing for increased effectiveness of conservation and management of the species.

Compact and Low-Cost Fluorescence Based Flow-Through Analyzer for Early-Stage Classification of Potentially Toxic Algae and in Situ Semiquantification.

The occurrence and intensity of (harmful) algal blooms (HABs) have increased through the years due to rapidly changing environmental conditions. At the same time, the demand for low-cost instrumentation has increased substantially, enabling the real-time monitoring and early-stage detection of HABs. To meet this challenge, we have developed a compact multi-wavelength fluorometer for less than 400 USD. This is possible by using readily available and low-cost optical and electronic components. Its modular design results in a highly versatile and flexible monitoring tool. The algae detection module enables a continuous identification and control of relevant algal groups based on their spectral characteristics with a detection limit of 10 cells per liter. Besides its usage as a benchtop module in the laboratory, the algae module has been integrated into submersible housings and applied in coastal environments. During its first in situ application in the Port of Genoa, seawater samples of mixed algal composition were used to demonstrate the successful discrimination of cyanobacteria and dinophytes as well-known toxin producing classes. Fabrication, operation, and performance as well as its first in situ application are addressed.

Analysis of Optimal Sequential State Discrimination for Linearly Independent Pure Quantum States

Recently, J. A. Bergou et al. proposed sequential state discrimination as a new quantum state discrimination scheme. In the scheme, by the successful sequential discrimination of a qubit state, receivers Bob and Charlie can share the information of the qubit prepared by a sender Alice. A merit of the scheme is that a quantum channel is established between Bob and Charlie, but a classical communication is not allowed. In this report, we present a method for extending the original sequential state discrimination of two qubit states to a scheme of N linearly independent pure quantum states. Specifically, we obtain the conditions for the sequential state discrimination of N = 3 pure quantum states. We can analytically provide conditions when there is a special symmetry among N = 3 linearly independent pure quantum states. Additionally, we show that the scenario proposed in this study can be applied to quantum key distribution. Furthermore, we show that the sequential state discrimination of three qutrit states performs better than the strategy of probabilistic quantum cloning.

Geochemical Discrimination and Characteristics of Magmatic Tectonic Settings: A Machine‐Learning‐Based Approach

AbstractGeochemically discriminating between magmatism in different tectonic settings remains a fundamental part of understanding the processes of magma generation within the Earth's mantle. Here we present an approach where machine learning (ML) methods are used for quantitative tectonic discrimination and feature selection using global geochemical data sets containing data for volcanic rocks generated in eight different tectonic settings. This study uses support vector machine, random forest, and sparse multinomial regression (SMR) approaches. All these ML methods with data for 24 elements and five isotopic ratios allowed the successful geochemical discrimination between igneous rocks formed in eight different tectonic settings with a discriminant ratio better than 83% for all settings barring oceanic plateaus and back‐arc basins. SMR is a particularly powerful and interpretable ML method because it quantitatively identifies geochemical signatures that characterize the tectonic settings of interest and the characteristics of each sample as a probability of the membership of the sample for each setting. We also present the most representative basalt composition for each tectonic setting. The new data provide reference points for future geochemical discussions. Our results indicate that at least 17 elements and isotopic ratios are required to characterize each tectonic setting, suggesting that geochemical tectonic discrimination cannot be achieved using only a small number of elemental compositions and/or isotopic ratios. The results show that volcanic rocks formed in different tectonic settings have unique geochemical signatures, indicating that both volcanic rock geochemistry and magma generation processes are closely connected to the tectonic setting.

Recovering task fMRI signals from highly under-sampled data with low-rank and temporal subspace constraints

Recent developments in highly accelerated fMRI data acquisition have employed low-rank and/or sparsity constraints for image reconstruction, as an alternative to conventional, time-independent parallel imaging. When under-sampling factors are high or the signals of interest are low-variance, however, functional data recovery can be poor or incomplete. We introduce a method for improving reconstruction fidelity using external constraints, like an experimental design matrix, to partially orient the estimated fMRI temporal subspace. Combining these external constraints with low-rank constraints introduces a new image reconstruction model that is analogous to using a mixture of subspace-decomposition (PCA/ICA) and regression (GLM) models in fMRI analysis.We show that this approach improves fMRI reconstruction quality in simulations and experimental data, focusing on the model problem of detecting subtle 1-s latency shifts between brain regions in a block-design task-fMRI experiment. Successful latency discrimination is shown at acceleration factors up to R = 16 in a radial-Cartesian acquisition. We show that this approach works with approximate, or not perfectly informative constraints, where the derived benefit is commensurate with the information content contained in the constraints. The proposed method extends low-rank approximation methods for under-sampled fMRI data acquisition by leveraging knowledge of expected task-based variance in the data, enabling improvements in the speed and efficiency of fMRI data acquisition without the loss of subtle features.

High-pulse energy supercontinuum laser for high-resolution spectroscopic photoacoustic imaging of lipids in the 1650-1850 nm region.

We propose a cost-effective high-pulse energy supercontinuum (SC) source based on a telecom range diode laser-based amplifier and a few meters of standard single-mode optical fiber, with a pulse energy density as high as ~25 nJ/nm in the 1650-1850 nm regime (factor >3 times higher than any SC source ever used in this wavelength range). We demonstrate how such an SC source combined with a tunable filter allows high-resolution spectroscopic photoacoustic imaging and the spectroscopy of lipids in the first overtone transition band of C-H bonds (1650-1850 nm). We show the successful discrimination of two different lipids (cholesterol and lipid in adipose tissue) and the photoacoustic cross-sectional scan of lipid-rich adipose tissue at three different locations. The proposed high-pulse energy SC laser paves a new direction towards compact, broadband and cost-effective source for spectroscopic photoacoustic imaging.

Comprehensive Fault Type Discrimination Concept for Bipolar Full-Bridge-Based MMC HVDC Systems with Dedicated Metallic Return

Bipolar HVDC transmission systems typically offer up to 50% transmission redundancy in case of single pole-to-ground line faults. Continuous operation during permanent multiconductor faults, however, is not permitted, emphasizing the need for accurate fault type discrimination in such applications. For bipolar schemes with a dedicated metallic return (DMR) conductor, discrimination of pole–ground faults and pole–DMR faults represents a challenging task due to their similar electrical characteristics. In this paper, several approaches to detect DMR fault involvement are identified and analyzed, comprising both noninvasive methods based on measurement data and invasive methods based on DC current control strategies of full-bridge VSC-MMC stations (Modular Multilevel Converter based on Voltage Source Converter technology). An exemplified point-to-point HVDC system is modeled in PSCAD/EMTDC and extensive simulation studies are carried out. Based on the obtained results, a comprehensive fault type discrimination concept for permanent line faults is developed, which can be incorporated into fault handling schemes. Thus, successful fault type discrimination can be performed for all relevant fault types, even under exceptional fault conditions.

Abstract A202: Evaluating the combination of anti-PD-1 and nitric oxide synthase inhibition therapy in 12 triple-negative breast cancer patient-derived xenografts using a human-derived immune system model

Abstract Introduction: Anti-PD-1 monotherapy has shown a limited therapeutic benefit in breast cancer (TNBC). In triple-negative breast cancer (TNBC), inducible nitric oxide synthase (iNOS) is associated with poor survival due to increased tumor aggressiveness. In cancer, iNOS product, nitric oxide (NO), is associated with the establishment of an immunosuppressive environment. Most patients do not, or only incompletely, respond to PD-1 inhibitors due to cancer-related immunosuppression. The purpose of the present study is to evaluate combination therapy anti-PD-1 plus NOS inhibition as a feasible combination for TNBC. Methods: BT-549, SUM-159, SUM-157, HCC-70, MDA-MB-231, and MDA-MD-468 TNBC cell lines and MDA-MB-436 HER2+ cell line were treated with NOS inhibition therapy (L-NMMA, L; 4mM) + amlodipine (A; 5 µM) daily for 48 h; Western blot was used to measure PDL-1 expression. BALBc mice growing orthotopically injected 4t1 cells were treated weekly with: 1) vehicle (saline, oral gavage/ Rat IgG2 i.p.); 2) NOS inhibitor [L-NMMA (200 mg/kg oral gavage)/mlodipine (10 mg/kg i.p.) on days 1-5]; 3) anti-PD-1 antibody (10 mg/kg i.p.; BioXcell Clone: RMP1-14, on days 1, 3, 5; or 4) NOS inhibitor + anti-PD-1. Humanized mice were developed by injecting hematopoietic stem cells (HSC, CD34+) into the tail vein of irradiated NOD-scid IL2Rγnull (NSG) immunodeficient mice. Six weeks later, human HSC engraftment was assessed by expression of human CD45, CD3, and CD20 in blood. Mice were sorted and TNBC patient-derived xenografts (PDXs) representing 12 different patients were implanted into the mammary fat pad (3 mice per PDX). Mice were sorted and treated weekly with: 1) vehicle; 2) pembrolizumab (anti-PD-1, 200 µg, day 1, IV); and 3) pembrolizumab/NOS inhibitor. Results: NOS inhibition increased PD-L1 levels in 6 out of 8 breast cancer cell lines. 4T1 syngeneic model was used to assess the potential benefit of NOS inhibition in combination with an anti-PD-1 therapy. No significant differences in tumor growth were observed between the single-agent therapies and the control group, but the combination anti-PD1/NOS inhibition resulted in a significant reduction in tumor growth. In the immune-humanized TNBC PDXs, 40% responded to anti-PD1 monotherapy, 60% had an improved response in combination with NOS inhibition, and 33% responded only to combination therapy. In total, 66% of the PDXs analyzed benefited from combination therapy. Conclusion: NOS inhibition upregulates PDL-1 expression in TNBC cell lines. Furthermore, coupling NOS inhibition with anti-PD1 therapy improved the antibody response. Additional analyses are currently in progress to identify biomarkers that will allow successful discrimination of responders and nonresponders. This combination treatment may benefit cancer patients who do not respond to anti-PD-1 monotherapy. Citation Format: Daniel Davila-Gonzalez, Roberto R. Rosato, Bhuvanesh Dave, Dong Soon Choi, Wie Qian, Anthony J. Kozielski, Wen Chen, Joe E. Ensor, Jenny C. Chang. Evaluating the combination of anti-PD-1 and nitric oxide synthase inhibition therapy in 12 triple-negative breast cancer patient-derived xenografts using a human-derived immune system model [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A202.

Стратегии референции в диалогах об этнических лицах: топосемантический экспресс-анализ

In the experimental study in identification/discrimination of ethnic faces by dyads, the phenomena of mutual assimilation of the cognitive processes in communication partners were investigated. It is shown that the phenomenon of assimilation can be detected while comparing the partners’ “isostatic” fixation pat¬terns, which characterize the processes of visual attention and perception. At the same time, the lack of synchronization of the isostatic pattern between interlocutors may indicate a lower cognitive complexity of discrimination and a higher probability of successful discrimination. A tendency is observed for producing a more detailed reference structure of the dialogue in the conditions allowing better discrimination, and for less detailed dialogue when the communication partners interpret each other’s verbalizations as indicating that there are no differences in perceived ethnic faces.

Application of FTIR Spectroscopy for Assessment of Green Coffee Beans According to Their Origin

Samples of green coffee beans originating from five different countries were ground and analyzed using FTIR spectra in the region of 600–4000 cm–1. Successful discrimination of each coffee type based on their origin was achieved applying a PCA algorithm on the obtained IR spectra for all samples. PCA loading plots show that the IR bands at 2850, 2920, and 1745 cm–1 corresponding to the symmetric, and antisymmetric vibrations of CH2 and the stretching vibration of C=O bond in ester, respectively, are the most significant peaks in distinguishing the origin of the above coffee samples.

Use of FTIR spectroscopy and chemometrics for the classification of carobs origin

Carob samples from seven different Mediterranean countries (Cyprus, Greece, Italy, Spain, Turkey, Jordan and Palestine) were analyzed using Fourier Transform Infrared (FTIR) spectroscopy. Seed and flesh samples of indigenous and foreign cultivars, both authentic and commercial, were examined. The spectra were recorded in transmittance mode from KBr pellets. The data were compressed and further processed statistically using multivariate chemometric techniques, including Principal Component Analysis (PCA), Cluster Analysis (CA), Partial Least Squares (PLS) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA). Specifically, unsupervised PCA framed the importance of the variety of carobs, while supervised analysis highlighted the contribution of the geographical origin. Best classification models were achieved with PLS regression on first derivative spectra, giving an overall correct classification. Thus, the applied methodology enabled the differentiation of carobs flesh and seed per their origin. Our results appear to suggest that this method is a rapid and powerful tool for the successful discrimination of carobs origin and type.

Discrimination of irradiated MOX fuel from UOX fuel by multivariate statistical analysis of simulated activities of gamma-emitting isotopes

This paper investigates how concentrations of certain fission products and their related gamma-ray emissions can be used to discriminate between uranium oxide (UOX) and mixed oxide (MOX) type fuel. Discrimination of irradiated MOX fuel from irradiated UOX fuel is important in nuclear facilities and for transport of nuclear fuel, for purposes of both criticality safety and nuclear safeguards. Although facility operators keep records on the identity and properties of each fuel, tools for nuclear safeguards inspectors that enable independent verification of the fuel are critical in the recovery of continuity of knowledge, should it be lost.A discrimination methodology for classification of UOX and MOX fuel, based on passive gamma-ray spectroscopy data and multivariate analysis methods, is presented. Nuclear fuels and their gamma-ray emissions were simulated in the Monte Carlo code Serpent, and the resulting data was used as input to train seven different multivariate classification techniques. The trained classifiers were subsequently implemented and evaluated with respect to their capabilities to correctly predict the classes of unknown fuel items.The best results concerning successful discrimination of UOX and MOX-fuel were acquired when using non-linear classification techniques, such as the k nearest neighbors method and the Gaussian kernel support vector machine. For fuel with cooling times up to 20 years, when it is considered that gamma-rays from the isotope 134Cs can still be efficiently measured, success rates of 100% were obtained. A sensitivity analysis indicated that these methods were also robust.

A Hand-Held Optoelectronic Nose for the Identification of Liquors.

Successful discrimination of 14 representative liquors (including scotch, bourbon and rye whiskies, brandy, and vodka) was achieved using a 36-element colorimetric sensor array comprising multiple classes of cross-reactive, chemically responsive inks. In combination with a palm-sized image analyzer, the sensor array permits real-time identification of liquor products based on vapor analysis within 2 min. Changes in sensor spot colors before and after exposure to the vapors of the liquors that are partially oxidized as they are pumped over the sensor array provides a unique color difference pattern for each analyte. Facile identification of each liquor was demonstrated using several different multivariate analyses of the digital data library, including principal component, hierarchical cluster, and support vector machine analysis. The sensor array is also able to detect dilution (i.e., "watering") of liquors even down to 1% addition of water. This colorimetric sensor array is a promising portable adjunct to other available techniques for quality assurance of liquors and other alcoholic beverages.

Statistical approaches to the discrimination of crust- and mantle-derived low-Cr garnet – Major-element-based methods and their application in diamond exploration

In diamond exploration, the accurate distinction between garnets from the crust or mantle, or from those having a cognate origin with kimberlite (low-Cr megacrysts), is important for the assessment of indicator mineral samples; misclassifications potentially result in costly misdirection of exploration efforts. Existing literature databases and graphical classification schemes for garnets suffer from a paucity of craton-derived, lower-crustal garnets that – as shown here – are among the most difficult to distinguish from garnets of mantle origin. To improve this situation, a large database of new and literature garnet major element analyses has been compiled. Using this dataset, it is shown that the conventionally used Mg# (Mg/(Mg+Fe)) vs. Ca# (Ca/(Mg+Ca)) plot (Schulze, 2003) for discrimination of crust and mantle garnets results in significant overlap (39.2% crustal failure rate using our dataset). We propose a new graphical classification scheme that uses the parameters ln(Ti/Si) and ln(Mg/Fe) to discriminate low-Cr garnets of crust origin from those of a mantle eclogite-pyroxenite origin with an error rate of 10.1±2.1% at the 95% confidence level (assessed via K-fold cross-validation with ten random test datasets), significantly lower than existing methods.Multivariate statistical solutions, which incorporate a wide selection of geochemical variables, represent additional possibilities for discrimination. Using our new database, logistic regression (LR) and linear discriminant analysis (LDA) approaches are evaluated and new crust-mantle garnet discrimination schemes derived. The resulting solutions, using a wide variety of cations in garnet, provide lower misclassification rates than existing literature schemes. Both LR and LDA are successful discrimination techniques with error rates for the discrimination of crust from mantle eclogite-pyroxenite of 7.5±1.9% and 8.2±2.3%, respectively. LR, however, involves fewer stipulations about the distribution of training data (i.e., it is more “robust”) and can return an estimate for probability of classification certainty for single garnets. New data from diamond exploration programs can be readily classified using these new graphical and statistical methods. As the discrimination of low-Cr megacrysts from mantle eclogite-pyroxenite is not the focus of this study, we recommend the method of Schulze (2003) or Grütter et al. (2004) for low-Cr megacryst discrimination to identify megacrysts in the “mantle” suite. Runstreams for our LDA and LR approaches using the freeware “R” are provided for quick implementation.